Early-Stage Alzheimer’s Disease Prediction Using Machine Learning Models
Open accessAbstract
Alzheimer's disease (AD) is the leading cause of dementia in older adults. There is currently a lot of interest in applying machine learning to find out metabolic diseases like Alzheimer's and Diabetes that affect a large population of people around the world. Their incidence rates are increasing at an alarming rate every year. In Alzheimer's disease, the brain is affected by neurodegenerative changes. As our aging population increases, more and more individuals, their families, and healthcare will experience diseases that affect memory and functioning. These effects will be profound on the social, financial, and economic fronts. In its early stages, Alzheimer's disease is hard to predict. A treatment given at an early stage of AD is more effective, and it causes fewer minor damage than a treatment done at a later stage. Several techniques such as Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, and Voting classifiers have been employed to identify the best parameters for Alzheimer's disease prediction. Predictions of Alzheimer's disease are based on Open Access Series of Imaging Studies (OASIS) data, and performance is measured with parameters like Precision, Recall, Accuracy, and F1-score for ML models. The proposed classification scheme can be used by clinicians to make diagnoses of these diseases. It is highly beneficial to lower annual mortality rates of Alzheimer's disease in early diagnosis with these ML algorithms. The proposed work shows better results with the best validation average accuracy of 83% on the test data of AD. This test accuracy score is significantly higher in comparison with existing works.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The early-stage Alzheimer's prediction models achieved 83% accuracy on test data.
“Achieved 83% accuracy on test data”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [65]
Men are more likely to have dementia than women.
“likely to have dementia than women. Men are more”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [65]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
RETRACTED ARTICLE: Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis
Gadekallu, T. R., Maddikunta, P. K. R., Lakshmanna, K., Rajput, D. S., Kaluri, R., & Srivastava, G. (2019) · Evolutionary Intelligence · doi:10.1007/s12065-019-00327-1 · Cited by 407 · see citing works
RetractedCorrespondence - Tranexamic acid for traumatic brain injury
(2005) · London School of Economics and Political Science Theses Online (London School of Economics and Political Science) · doi:10.1016/s0140-6736 · Cited by 11,894 · see citing works
Dementia prevention, intervention, and care
Livingston, G., Sommerlad, A., Orgeta, V., Costafreda, S. G., Huntley, J., Ames, D., Ballard, C., Banerjee, S., Burns, A., Cohen-Mansfield, J., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)31363-6 · Cited by 6,072 · see citing works
Deckers, K., van Boxtel, M. P. J., Schiepers, O. J. G., de Vugt, M., Muñoz Sánchez, J. L., Anstey, K. J., Brayne, C., Dartigues, J.-F., Engedal, K., Kivipelto, M., et al. (2015) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.4245 · Cited by 534 · see citing works
Deep Learning Approach for Early Detection of Alzheimer’s Disease
Helaly, H. A., Badawy, M., & Haikal, A. Y. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09946-2 · Cited by 397 · see citing works
Khan, P., Kader, M. F., Islam, S. M. R., Rahman, A. B., Kamal, M. S., Toha, M. U., & Kwak, K. S. (2021) · IEEE Access · doi:10.1109/access.2021.3062484 · Cited by 294 · see citing works
Deep neural networks to predict diabetic retinopathy
Gadekallu, T. R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P. K. R., & Srivastava, G. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01963-7 · Cited by 285 · see citing works
Martínez-Murcia, F. J., Ortíz, A., Górriz, J. M., Ramı́rez, J., & Castillo-Barnés, D. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2914970 · Cited by 252 · see citing works
Lifestyle for Brain Health (LIBRA): a new model for dementia prevention
Schiepers, O. J. G., Köhler, S., Deckers, K., Irving, K., O'Donnell, C. A., van den Akker, M., Verhey, F. R. J., Vos, S. J. B., de Vugt, M. E., & van Boxtel, M. P. J. (2018) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.4700 · Cited by 208 · see citing works
Optimized dynamic storage of data (ODSD) in IoT based on blockchain for wireless sensor networks
Khalaf, O. I., & Abdulsahib, G. M. (2021) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-021-01115-4 · Cited by 175 · see citing works
Self-citationAutomated cognitive health assessment in smart homes using machine learning
Javed, A. R., Fahad, L. G., Farhan, A. A., Abbas, S., Srivastava, G., Parizi, R. M., & Khan, M. S. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102572 · Cited by 174 · see citing works
Computational Modeling of Dementia Prediction Using Deep Neural Network: Analysis on OASIS Dataset
Basheer, S., Bhatia, S., & Sakri, S. B. (2021) · IEEE Access · doi:10.1109/access.2021.3066213 · Cited by 170 · see citing works
Vos, S. J. B., van Boxtel, M. P. J., Schiepers, O. J. G., Deckers, K., de Vugt, M., Carrière, I., Dartigues, J.-F., Peres, K., Artero, S., Ritchie, K., et al. (2017) · Journal of Alzheimer’s Disease · doi:10.3233/jad-161208 · Cited by 161 · see citing works
Dementia risk and prevention by targeting modifiable vascular risk factors
Tariq, S., & Barber, P. A. (2017) · Journal of Neurochemistry · doi:10.1111/jnc.14132 · Cited by 135 · see citing works
MRI Deep Learning-Based Solution for Alzheimer’s Disease Prediction
Saratxaga, C. L., Moya, I., Picón, A., Acosta, M., Moreno-Fernandez-de-Leceta, A., Garrote, E., & Bereciartúa-Pérez, A. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11090902 · Cited by 127 · see citing works
Optimization of Wireless Sensor Network Coverage using the Bee Algorithm.
Khalaf, O. I., Abdulsahib, G. M., & Sabbar, B. M. (2020) · Journal of information science and engineering · Cited by 122 · see citing works
Self-citationAn adaptive intelligent alarm system for wireless sensor network
Salman, A. D., Khalaf, O. I., & Abdulsaheb, G. M. (2019) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v15.i1.pp142-147 · Cited by 115 · see citing works
Self-citationKhalaf, O. I., Abdulsahib, G. M., Kasmaei, H. D., & Ogudo, K. A. (2019) · International Journal of e-Collaboration · doi:10.4018/ijec.2020010102 · Cited by 107 · see citing works
Self-citationOgudo, K. A., Nestor, D. M. J., Khalaf, O. I., & Kasmaei, H. D. (2019) · Symmetry · doi:10.3390/sym11040593 · Cited by 101 · see citing works
Self-citationModifiable Risk Factors and Prevention of Dementia
Yaffe, K. S. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.7299 · Cited by 95 · see citing works
Dhanamjayulu, C., Nizhal, U. N., Maddikunta, P. K. R., Gadekallu, T. R., Iwendi, C., Wei, C., & Xin, Q. (2021) · IET Image Processing · doi:10.1049/ipr2.12222 · Cited by 88 · see citing works
Srinivasu, P. N., Bhoi, A. K., Jhaveri, R. H., Gadekallu, T. R., & Bilal, M. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01122-x · Cited by 82 · see citing works
An Efficient Deep Neural Network Binary Classifier for Alzheimer’s Disease Classification
Prajapati, R., Khatri, U., & Kwon, G. (2021) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic51459.2021.9415212 · Cited by 66 · see citing works
Khalaf, O. I., & Abdulsahib, G. M. (2019) · Journal of information science and engineering · doi:10.6688/jise.201909_35(5).0010 · Cited by 65 · see citing works
Self-citationMachine Learning Framework for Implementing Alzheimer’s Disease
Sivakani, R., & Ansari, G. A. (2020) · doi:10.1109/iccsp48568.2020.9182220 · Cited by 63 · see citing works
Javed, A. R., Sarwar, M. U., Rehman, S. U., Khan, H. U., Al‐Otaibi, Y. D., & Alnumay, W. S. (2021) · Neural Processing Letters · doi:10.1007/s11063-020-10414-5 · Cited by 58 · see citing works
Machine Learning Techniques for Diagnostic Differentiation of Mild Cognitive Impairment and Dementia
Williams, J. A., Weakley, A. M., Cook, D. J., & Schmitter‐Edgecombe, M. (2013) · National Conference on Artificial Intelligence · Cited by 54 · see citing works
Comparison and Evaluation of Cloud Processing Models in Cloud-Based Networks
Abdulsahib, G. M., & Khalaf, O. I. (2019) · International Journal of Simulation Systems Science & Technology · doi:10.5013/ijssst.a.19.05.26 · Cited by 50 · see citing works
Self-citationAbdulsahib, G. M., & Khalaf, O. I. (2021) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.22059/jitm.2021.80359 · Cited by 40 · see citing works
Self-citationFeasibility Study of a Machine Learning Approach to Predict Dementia Progression
Chi, C., Oh, W., & Borson, S. (2015) · doi:10.1109/ichi.2015.68 · Cited by 23 · see citing works
Promoting modifiable risk factors for dementia: is there a role for general practice?
O’Donnell, C. A., Manera, V., Köhler, S., & Irving, K. (2015) · British Journal of General Practice · doi:10.3399/bjgp15x687241 · Cited by 21 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [9]Sulaiman N Abdulsahib G Khalaf O Mohammed MN. “Effect of Using Different Propagations of OLSR and DSDV Routing Protocols”, In Proceedings of the IEEE International Conference on Intelligent Systems Structureing and Simulation. (2014), pp. 540– 5.
- [14]National National Academies of Sciences Engineering and Medicine. Preventing cognitive decline and dementia: A way forward. London: The National Academies Press (2018). 28650595
- [22]Sudharsan M Thailambal G. Alzheimer's disease prediction using machine learning techniques and principal component analysis (PCA), Materials Today: Proceedings (2021).
Cited by
Sorted by citations
A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing
Bharany, S., Sharma, S., Khalaf, O. I., Abdulsahib, G. M., Al-Humaimeedy, A. S., Aldhyani, T. H. H., Maashi, M. S., & Alkahtani, H. S. (2022) · Sustainability · doi:10.3390/su14106256 · Cited by 257 · see citing works
Self-citationAn Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning
Mahmud, T., Barua, K., Habiba, S. U., Sharmen, N., Hossain, M. S., & Andersson, K. (2024) · Diagnostics · doi:10.3390/diagnostics14030345 · Cited by 161 · see citing works
Explainable AI-based Alzheimer’s prediction and management using multimodal data
Jahan, S., Abu Taher, K., Kaiser, M. S., Mahmud, M., Rahman, M. S., Hosen, A. S. M. S., & Ra, I.-H. (2023) · PLOS ONE · doi:10.1371/journal.pone.0294253 · Cited by 122 · see citing works
Sadr, H., Nazari, M., Khodaverdian, Z., Farzan, R., Yousefzadeh-Chabok, S., Ashoobi, M. T., Hemmati, H., Hendi, A., Ashraf, A., Pedram, M. M., et al. (2025) · European journal of medical research · doi:10.1186/s40001-025-02680-7 · Cited by 117 · see citing works
Badidi, E. (2023) · Future Internet · doi:10.3390/fi15110370 · Cited by 113 · see citing works
A Novel Approach Utilizing Machine Learning for the Early Diagnosis of Alzheimer's Disease
Uddin, K. M. M., Alam, M. J., Jannat-E-Anawar, Uddin, M. A., & Aryal, S. (2023) · Biomedical Materials & Devices · doi:10.1007/s44174-023-00078-9 · Cited by 101 · see citing works
Supervised Machine Learning Empowered Multifactorial Genetic Inheritance Disorder Prediction
Ghazal, T. M., Hamadi, H. A., Nasir, M. U., Rahman, A., Gollapalli, M. A. S., Zubair, M., Khan, M. A., & Yeun, C. Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1051388 · Cited by 95 · see citing works
Sisodia, P. S., Ameta, G., Kumar, Y., & Chaplot, N. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09870-0 · Cited by 77 · see citing works
Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System
Subahi, A. F., Khalaf, O. I., Alotaibi, Y. A., Natarajan, R., Mahadev, N., & Ramesh, T. (2022) · Sustainability · doi:10.3390/su142114208 · Cited by 68 · see citing works
Self-citationAlMohimeed, A., Saad, R. M. A., Mostafa, S., El-Rashidy, N. M., Farrag, S., Gaballah, A., Elaziz, M. A., El–Sappagh, S., & Saleh, H. (2023) · IEEE Access · doi:10.1109/access.2023.3328331 · Cited by 67 · see citing works
Khan, Y. F., Kaushik, B., Chowdhary, C. L., & Srivastava, G. (2022) · Diagnostics · doi:10.3390/diagnostics12123193 · Cited by 65 · see citing works
Anand, M. V., KiranBala, B., Srividhya, S. R., Kavitha, C., Younus, M., & Rahman, M. H. (2022) · Mobile Information Systems · doi:10.1155/2022/2436946 · Cited by 61 · see citing works
Self-citationHarnessing artificial intelligence for enhanced public health surveillance: a narrative review
Mendes, V. I. S., Mendes, B. M. F., Moura, R. P., Lourenço, I., Oliveira, M. F. A., Ng, K. L., & Pinto, C. S. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1601151 · Cited by 60 · see citing works
Hajjo, R., Sabbah, D. A., Abusara, O. H., & Bawab, A. Q. A. (2022) · Diagnostics · doi:10.3390/diagnostics12122975 · Cited by 55 · see citing works
Menagadevi, M., Devaraj, S., Madian, N., & Thiyagarajan, D. (2024) · Measurement · doi:10.1016/j.measurement.2023.114100 · Cited by 52 · see citing works
Hai, T., Zhou, J., Srividhya, S. R., Jain, S., Young, P., & Agrawal, S. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00294-6 · Cited by 51 · see citing works
Self-citationYousefi, M., Akhbari, M., Mohamadi, Z., Karami, S., Dasoomi, H., Atabi, A., Sarkeshikian, S. A., Dehaki, M. A., Bayati, H., Mashayekhi, N., et al. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1413071 · Cited by 51 · see citing works
Mirkin, S., & Albensi, B. C. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1094233 · Cited by 46 · see citing works
Peng, Q., Zeng, Q., Wang, F., Wu, X., Zhang, R., Shi, G., & Zhang, M. (2023) · ACS Nano · doi:10.1021/acsnano.3c08499 · Cited by 46 · see citing works
Federated Explainable AI-Based Alzheimer’s Disease Prediction With Multimodal Data
Jahan, S., Adib, M. R. S., Huda, S. M., Rahman, M. S., Kaiser, M. S., Hosen, A. S. M. S., Ghimire, D., & Park, M. J. (2025) · IEEE Access · doi:10.1109/access.2025.3547343 · Cited by 45 · see citing works
Artificial intelligence and neuroscience: An update on fascinating relationships
Gopinath, N. (2022) · Process Biochemistry · doi:10.1016/j.procbio.2022.12.011 · Cited by 44 · see citing works
Salehi, A. W., Gupta, G., Khan, S. B., Alkhaldi, N. A., & Verma, A. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100348 · Cited by 44 · see citing works
An Approach to Binary Classification of Alzheimer’s Disease Using LSTM
Salehi, W., Baglat, P., Gupta, G., Khan, S. B., Almusharraf, A., Alqahtani, A., & Kumar, A. (2023) · Bioengineering · doi:10.3390/bioengineering10080950 · Cited by 42 · see citing works
A machine learning model for Alzheimer's disease prediction
Rani, P., Lamba, R., Sachdeva, R. K., Kumar, K., & Iwendi, C. (2024) · IET Cyber-Physical Systems Theory & Applications · doi:10.1049/cps2.12090 · Cited by 41 · see citing works
A Logistic Regression and Decision Tree Based Hybrid Approach to Predict Alzheimer's Disease
Tushar, T., Patel, R. K., Aggarwal, E., Solanki, K., Dahiya, O., & Yadav, S. A. (2023) · International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) · doi:10.1109/cises58720.2023.10183466 · Cited by 38 · see citing works
A Transfer Learning Approach: Early Prediction of Alzheimer’s Disease on US Healthy Aging Dataset
Reddy, C. K. K., Rangarajan, A., Rangarajan, D., Shuaib, M., Jeribi, F., & Alam, S. (2024) · Mathematics · doi:10.3390/math12142204 · Cited by 36 · see citing works
Akter, S., Liu, Z., Simões, E. J., & Rao, P. R. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2025.100169 · Cited by 36 · see citing works
The role of machine learning in advancing precision medicine with feedback control
Zlobina, K., Jafari, M., Rolandi, M., & Gomez, M. M. (2022) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2022.101149 · Cited by 33 · see citing works
Realtime Anomaly Detection in Healthcare IoT: A Machine Learning-Driven Security Framework
Raje, E. A. V. V. (2024) · Journal of Electrical Systems · doi:10.52783/jes.700 · Cited by 32 · see citing works
SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis
Wang, J., Khan, M. A., Wang, S., & Zhang, Y. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.041191 · Cited by 32 · see citing works
Alshamlan, H., Alwassel, A., Banafa, A., & Alsaleem, L. (2024) · Diagnostics · doi:10.3390/diagnostics14192237 · Cited by 31 · see citing works
Kanthavel, R., Dhaya, R., Khalaf, O. I., Uddin, M., Chen, C., & Wu, C.-M. (2022) · Sensors · doi:10.3390/s22124475 · Cited by 27 · see citing works
Self-citationJaved, R., Abbas, T., Shahzad, T., Kanwal, K., Ramay, S. A., Khan, M. A., & Ouahada, K. (2025) · IEEE Access · doi:10.1109/access.2025.3525514 · Cited by 25 · see citing works
Saleh, H., El-Rashidy, N. M., Elaziz, M. A., Aseeri, A. O., & El–Sappagh, S. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00514-z · Cited by 24 · see citing works
Mahamud, E., Assaduzzaman, M., Islam, J., Fahad, N., Hossen, M. J., & Ramanathan, T. T. (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100240 · Cited by 24 · see citing works
Machine learning and IoT in healthcare: Recent advancements, challenges & future direction
Zonayed, M., Tasnim, R., Jhara, S. S., Mimona, M. A., Hussein, M. R., Mobarak, M. H., & Salma, U. (2025) · Advances in Biomarker Sciences and Technology · doi:10.1016/j.abst.2025.08.006 · Cited by 24 · see citing works
STCNN: Combining SMOTE-TOMEK With CNN for Imbalanced Classification of Alzheimer's Disease
Anjali, A., Singh, D., Pandey, O. J., & Dai, H. (2024) · IEEE Sensors Letters · doi:10.1109/lsens.2024.3357196 · Cited by 23 · see citing works
Srividhya, S. R., Kavitha, C., Lai, W., Mani, V., & Khalaf, O. I. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/9273233 · Cited by 23 · see citing works
Self-citationRetractedAlzheimer's Disease Prediction using Machine Learning and Transfer Learning Models
Amrutesh, A., G, G. B. C., Amruthamsh, A., Asha Rani, K. P., & Gowrishankar, S. (2022) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss57437.2022.10026365 · Cited by 23 · see citing works
Mazur, A., Costantino, H., Tom, P. A., Wilson, M. P., & Thompson, R. G. (2025) · The Annals of Family Medicine · doi:10.1370/afm.240091 · Cited by 22 · see citing works
Moghadam, M. P., Moghadam, Z. A., Qazani, M. R. C., Pławiak, P., & Alizadehsani, R. (2024) · IEEE Access · doi:10.1109/access.2024.3450970 · Cited by 22 · see citing works
Dubey, Y. K., Bhongade, A., Palsodkar, P., & Fulzele, P. R. (2024) · Diagnostics · doi:10.3390/diagnostics14242770 · Cited by 21 · see citing works
A Trust-Based Secure Neuro Fuzzy Clustering Technique for Mobile Ad Hoc Networks
Rajeswari, A. R., Lai, W., Kavitha, C., Kavin, B. P., & Srividhya, S. R. (2023) · Electronics · doi:10.3390/electronics12020274 · Cited by 21 · see citing works
Self-citationComparison of Classification Algorithms for Alzheimer’s Disease Prediction
Saxena, H., Joshi, D., Singh, H., & Anand, R. (2022) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc56933.2022.10053163 · Cited by 19 · see citing works
Artificial intelligence in dementia
Richardson, A., Robbins, C. B., Wisely, C. E., Henao, R., Grewal, D. S., & Fekrat, S. (2022) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000881 · Cited by 18 · see citing works
Logistic random forest boosting technique for Alzheimer’s diagnosis
Shastry, K. A., & Sattar, S. A. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01187-w · Cited by 18 · see citing works
ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach
Jumaili, M. L. F., & Sonuç, E. (2025) · SLAS TECHNOLOGY · doi:10.1016/j.slast.2025.100298 · Cited by 18 · see citing works
AI-assisted multi-OMICS analysis reveals new markers for the prediction of AD
Latifi‐Navid, H., Mokhtari, S., Taghizadeh, S., Moradi, F., Poostforoush-Fard, D., Alijanpour, S., & Aghanoori, M. (2025) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2025.167925 · Cited by 17 · see citing works
Ahmad, I., Siddiqi, M. H., Alhujaili, S. F., & Alrowaili, Z. A. (2023) · Healthcare · doi:10.3390/healthcare11182551 · Cited by 17 · see citing works
Saeed, F. (2024) · American Journal of Biomedical Science & Research · doi:10.34297/ajbsr.2024.22.003014 · Cited by 17 · see citing works
Hossain, M. K., Ashraf, A., Islam, M. M., Sourav, S., & Shimul, M. M. H. (2025) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70162 · Cited by 15 · see citing works
Ensemble-of-classifiers-based approach for early Alzheimer’s Disease detection
Rajasree, R. S., & Rajakumari, S. B. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16023-3 · Cited by 15 · see citing works
Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier
Mahmud, S. M. H., Ali, M. M., Shahriar, M. F., Al-Zahrani, F. A., Ahmed, K., Nandi, D., & Bui, F. M. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.039020 · Cited by 15 · see citing works
The Human Voice as a Digital Health Solution Leveraging Artificial Intelligence
Muddaloor, P., Baraskar, B., Shah, H., Gopalakrishnan, K., Sood, D., Pasupuleti, P. C., Singh, A. P., Mitra, D., Hoskote, S. S., Iyer, V., et al. (2025) · Sensors · doi:10.3390/s25113424 · Cited by 14 · see citing works
Alzheimer’s disease multiclass detection through deep learning models and post-processing heuristics
Rahbani, R. G. A., Ioannou, A., & Wang, T. (2024) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2024.2383219 · Cited by 14 · see citing works
A machine learning approach for text pattern diagnosis in mental health consultations
Juanita, S., Daeli, A. H., Syafrullah, M., Anggraeni, W., & Purnomo, M. H. (2025) · Decision Analytics Journal · doi:10.1016/j.dajour.2025.100572 · Cited by 14 · see citing works
Detection of dementia progression from functional activities data using machine learning techniques
Thabtah, F., Ong, S., & Peebles, D. (2022) · Intelligent Decision Technologies · doi:10.3233/idt-220054 · Cited by 14 · see citing works
Li, Z., Wang, Y., Jiang, Z., Luo, Z., Wu, J., & Toe, T. T. (2023) · International Symposium on Computer Technology and Information Science (ISCTIS) · doi:10.1109/isctis58954.2023.10213182 · Cited by 14 · see citing works
Masud, M., Almars, A. M., Rokaya, M., Meshref, H., Gad, I., & Atlam, E.-S. (2024) · Journal of Disability Research · doi:10.57197/jdr-2024-0042 · Cited by 14 · see citing works
Zuo, L., Liu, G., Fang, Z., Zhao, J., Li, J., Zheng, S., & Su, X. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.112593 · Cited by 13 · see citing works
Using CatBoost and Other Supervised Machine Learning Algorithms to Predict Alzheimer's Disease
An, J. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00265 · Cited by 12 · see citing works
Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease
Alatrany, A. S., Hussain, A. J., Alatrany, S. S. J., Mustafina, J., & Al‐Jumeily, D. (2023) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese58274.2023.10099655 · Cited by 12 · see citing works
Khan, A., Zubair, S., Shuaib, M., Sheneamer, A. M., Alam, S., & Assiri, B. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1391465 · Cited by 12 · see citing works
Unlocking biological complexity: the role of machine learning in integrative multi-omics
Kumar, R., Ruhel, R., & van Wijnen, A. J. (2024) · Academia Biology · doi:10.20935/acadbiol7428 · Cited by 12 · see citing works
Babu, B., Parvathy, G., Bawa, F. S. M., Gill, G. S., Patel, J., Sibia, D. S., Sureddi, J., & Patel, V. (2024) · Cureus · doi:10.7759/cureus.75389 · Cited by 12 · see citing works
Sakthimohan, M., Rani, G. E., Amuthaguka, D., & Sornalakshmi, M. (2025) · Waves in Random and Complex Media · doi:10.1080/17455030.2025.2535959 · Cited by 12 · see citing works
Gu, Z., Ge, B., Wang, Y., Gong, Y., & Qi, M. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1609063 · Cited by 12 · see citing works
A lightweight CNN for colon cancer tissue classification and visualization
Li, J., Goh, W., & Jhanjhi, N. Z. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1659010 · Cited by 12 · see citing works
Anitha, K., Radhika, S., Kavitha, C., Lai, W., Srividhya, S. R., & Naresh, K. (2022) · Biomedicines · doi:10.3390/biomedicines10102438 · Cited by 12 · see citing works
Self-citationCabanillas-Carbonell, M., & Zapata-Paulini, J. E. (2025) · Brain Behavior & Immunity - Health · doi:10.1016/j.bbih.2025.100957 · Cited by 11 · see citing works
Radhika, S., Anitha, K., Kavitha, C., Lai, W., & Srividhya, S. R. (2022) · Electronics · doi:10.3390/electronics12010123 · Cited by 11 · see citing works
Self-citationDisease Prediction Based on Symptoms Using Ensemble and Hybrid Machine Learning Models
Bharathi Mohan, G., Kumar, R. P., Vardhan, H., Veda Sampreetha, M., Siva Sairam, A., Sd., S., Sriram, K., & Kakarla, Y. (2024) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence60223.2024.10463480 · Cited by 11 · see citing works
An Interpretable PyCaret Approach for Alzheimer's Disease Prediction
P., A., & GUNASUNDARI, R. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.655 · Cited by 11 · see citing works
Huang, C.-C., Kuo, W.-Y., Shen, Y., Shen, Y., Lin, H.-J., Hsu, C.-C., Hsu, C., & Huang, C. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105590 · Cited by 10 · see citing works
Advancing Alzheimer's Therapy: Computational strategies and treatment innovations
Paul, J. K., Malik, A., Azmal, M., Gulzar, T., Afghan, M. T. R., Talukder, O. F., Shahzadi, S., & Ghosh, A. (2025) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2025.02.002 · Cited by 10 · see citing works
Integrating NLP and LLMs to discover biomarkers and mechanisms in Alzheimer's disease
Song, J., Huang, J., & Liu, R. (2025) · SLAS TECHNOLOGY · doi:10.1016/j.slast.2025.100257 · Cited by 10 · see citing works
Lakshmi, S. G., Kumar, C., & Kumar, P. D. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.441 · Cited by 10 · see citing works
A Comparative Study of Machine Learning Algorithms for Early Prediction of Alzheimer's Disease
Akilandeswari, A. (2025) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei65986.2025.11013500 · Cited by 10 · see citing works
ML in Medical Imaging Informatics Recent Trends and Future Directions
Selvaragini, S., & Rajaram, G. (2025) · International Conference on Communications and Signal Processing (ICCSP) · doi:10.1109/iccsp64183.2025.11088771 · Cited by 10 · see citing works
Granov, R., Vedad, S., Wang, S.-H., Durham, A., Shah, D., & Pasinetti, G. M. (2024) · Molecular Neurobiology · doi:10.1007/s12035-024-04339-6 · Cited by 10 · see citing works
Alam, M. S. B., Lameesa, A., Sharmin, S., Afrin, S., Ahmed, S. F., Nikoo, M. R., & Gandomi, A. H. (2025) · Digital Communications and Networks · doi:10.1016/j.dcan.2025.04.001 · Cited by 9 · see citing works
Machine learning to detect Alzheimer's disease with data on drugs and diagnoses
Wallensten, J., Wachtler, C., Bogdanović, N., Olofsson, A., Kivipelto, M., Jönsson, L., Petrović, P., & Carlsson, A. C. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2025.100115 · Cited by 9 · see citing works
Pandey, P. K., Khan, S. B., Pruthi, J., Albalawi, E. M., Algarni, A. D., & Almusharraf, A. I. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1587026 · Cited by 9 · see citing works
Rajayyan, S., & Mustafa, S. M. M. (2023) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v13i4.pp4623-4632 · Cited by 9 · see citing works
Early Diagnosis of Alzheimer’s Disease Using Machine Learning Methods
Demir, S., & SELVİTOPİ, H. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.201 · Cited by 9 · see citing works
Alzheimer's Detection and Classification Using Fine‐Tuned Convolutional Neural Network
Ali, A., Sarvamangala, D. R., Sundaram, A. M., & Rashmi, C. (2023) · Wiley eBooks · doi:10.1002/9781394175130.ch8 · Cited by 9 · see citing works
Accelerating Alzheimer's Research with Machine Learning Models for Improved Detection
Kavitha, G., Sudha, K., Jayasutha, D., Sivaraman, V., Nalini, M., & Siva Subramanian, R. (2023) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca58529.2023.10395855 · Cited by 8 · see citing works
Deep Learning and AI for Alzheimer’s Disease Prediction
Gagneja, S., Kaur, B., Gupta, R., & Kumar, V. (2024) · International Conference for Innovation in Technology (INOCON) · doi:10.1109/inocon60754.2024.10512186 · Cited by 8 · see citing works
Mao, M., Komes, D., Zhao, S., Pjanović, R., Wang, D., & Wang, Y. (2025) · The Innovation Medicine · doi:10.59717/j.xinn-med.2025.100121 · Cited by 8 · see citing works
Reddy, C. K. K., Ahmed, H. I., Mohzary, M., Singh, T. M., Shuaib, M., Alam, S., & Alnami, H. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1568312 · Cited by 8 · see citing works
IoT for Continuous Physiological Parameters Monitoring in Healthcare: A Review
Mila, S. A., & Ray, S. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3597861 · Cited by 8 · see citing works
Khan, A. H., Ali, D., Ahmed, S., Alhumam, A., Khan, M. F., & Siddiqui, S. Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-19395-8 · Cited by 8 · see citing works
Alam, T., Suchi, J., Saha, A., Pathan, F., Das, A., Kabir, M., & Mridha, M. F. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-025-11476-4 · Cited by 8 · see citing works
Qu, S., Han, L., Kuffer, M., & Li, L. (2026) · Ecological Indicators · doi:10.1016/j.ecolind.2026.114709 · Cited by 8 · see citing works
Improved Classification of Alzheimer’s Disease With Convolutional Neural Networks
Muhammed, S., Upadhya, J., Poudel, S., Hasan, M. K., Donthula, K., Vargas, J., Ranganathan, J., & Poudel, K. N. (2023) · IEEE Signal Processing in Medicine and Biology Symposium · doi:10.1109/spmb59478.2023.10372725 · Cited by 8 · see citing works
A Review on the Use of Modern Computational Methods in Alzheimer’s Disease-Detection and Prediction
De, A., Mishra, T. K., Saraf, S., Tripathy, B. K., & Reddy, S. S. (2023) · Current Alzheimer Research · doi:10.2174/0115672050301514240307071217 · Cited by 8 · see citing works
Francis, A., Pandian, S. I. A., Sagayam, K. M., Dang, L., Anitha, J., Dinh, L., Pomplun, M., & Dang, H. (2024) · Pattern Analysis and Applications · doi:10.1007/s10044-024-01280-1 · Cited by 8 · see citing works
Rapid Alzheimer's Disease Diagnosis Using Advanced Artificial Intelligence Algorithms
Samad, A., & Aydı, E. S. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24jun1915 · Cited by 8 · see citing works
Transforming neurodegenerative disorder care with machine learning: Strategies and applications
Galal, A. A., Moustafa, A. A., & Salama, M. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.03.036 · Cited by 8 · see citing works
IMapC: Inner MAPping Combiner to Enhance the Performance of MapReduce in Hadoop
Kavitha, C., Srividhya, S. R., Lai, W., & Mani, V. (2022) · Electronics · doi:10.3390/electronics11101599 · Cited by 8 · see citing works
Self-citation
- Authors
- Kavitha, C., Mani, V., Srividhya, S. R., Khalaf, O. I., & Tavera Romero, C. A.
- Affiliations
- Sathyabama Institute of Science and Technology (IN)
- Nahrain University (IQ)
- Corresponding author
- Kavitha, C.
- Venue
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Published
- 2022-03-03
- Volume
- 10
- Pages
- 853294-853294
- ISSN
- 2296-2565 (ISSN-L)
- PMC
- PMC8927715
- OpenAlex
- W4214898695
- Cited by
- 373
- References
- 30
- Citations by year
- 2026: 64 · 2025: 135 · 2024: 90 · 2023: 56 · 2022: 28
- FWCI
- 50.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universidad Santiago de Cali (01-2021)
- National Institute on Aging (P01AG003991)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, dementia, OASIS dataset, neuroimaging, machine learning classification, random forest, gradient boosting, support vector machine, decision tree, voting classifier, early diagnosis
- MeSH terms
- Machine Learning, Aged, Alzheimer Disease, Brain, Humans, Magnetic Resonance Imaging, Cognitive Dysfunction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
