Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus

SMOTE synthetic minority over-sampling technique

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865

Data Mining: Practical Machine Learning Tools and Techniques

Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543

Classification and Regression Trees.

Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867

Untitled work

(1992) · Cited by 23,431

Classification and Regression Trees.

Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030

International Diabetes Federation 2017

Carracher, A. M., Marathe, P. H., & Close, K. L. (2018) · Journal of Diabetes · doi:10.1111/1753-0407.12644 · Cited by 13,379

Classification and regression trees

Praagman, J. (1985) · European Journal of Operational Research · doi:10.1016/0377-2217(85)90321-2 · Cited by 10,053

Data Mining

Witten, I. H. (2008) · Cited by 9,143

Introduction to Data Mining

Chiu, S., & Tavella, D. (2008) · Routledge eBooks · doi:10.4324/9780080878096-12 · Cited by 6,987

Metabolic syndrome—a new world‐wide definition. A Consensus Statement from the International Diabetes Federation

Alberti, K. G. M. M., Zimmet, P., & Shaw, J. (2006) · Diabetic Medicine · doi:10.1111/j.1464-5491.2006.01858.x · Cited by 6,597

SUPPORT VECTOR MACHINES COMBINED WITH FEATURE SELECTION FOR DIABETES DIAGNOSIS

Akbulut, F. P., & Akan, A. (2017) · DergiPark (Istanbul University) · Cited by 8

Clinical Decision Support Systems and Predictive Analytics

Lourdusamy, R., & Mattam, X. J. (2020) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-40850-3_14 · Cited by 7

Artificial intelligence approaches to physiological parameter analysis in the monitoring and treatment of non-communicable diseases: A review

Ramirez-Bautista, J. A., Chaparro-Cárdenas, S. L., Esmer, C., & Huerta-Ruelas, J. A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105463 · Cited by 7

Data mining applications in risk research: A systematic literature review

Sıcakyüz, Ç., Edalatpanah, S. A., & Pamucar, D. S. (2024) · International Journal of Knowledge-based and Intelligent Engineering Systems · doi:10.1177/13272314241296866 · Cited by 12

Identification of the Risk Factors of Type II Diabetic Data Based Support Vector Machine Classifiers upon Varied Kernel Functions

Abdullah, A. S., Gayathri, N., Selvakumar, S., & Kumar, S. R. (2018) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-319-71767-8_42 · Cited by 7

Rapid protocols to support COVID-19 clinical diagnosis based on hematological parameters

Gomes, J. C., Barbosa, V. A. D. F., de Santana, M. A., de Lima, C. L., Calado, R. B., Júnior, C. R. B., Albuquerque, J. E. D. A., de Souza, R. G., de Araújo, R. J. E., Moreno, G. M. M., et al. (2023) · Research on Biomedical Engineering · doi:10.1007/s42600-023-00286-8 · Cited by 7

Detection of Type 2 Diabetes Mellitus Disease with Data Mining Approach Using Support Vector Machine

Tama, B. A. (2010) · Cited by 7

Personal Health Systems for Diabetes Management, Early Diagnosis and Prevention

Zarkogianni, K., & Nikita, K. S. (2015) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/978-1-4666-8828-5.ch022 · Cited by 7

Early Diagnosis of Diabetes Mellitus Using Data Mining and Classification Techniques

Dezfuli, S. A. M., Dezfuli, S. V. M., Dezfuli, S. V. M., & Kiani, Y. (2019) · Jundishapur Journal of Chronic Disease Care · doi:10.5812/jjcdc.94173 · Cited by 7

Bilingual Chatbot For Covid-19 Detection Based on Symptoms Using Rasa NLU

Mellah, Y., Bouchentouf, T., Rahmoun, N., & Rahmoun, M. (2022) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset52964.2022.9738103 · Cited by 7

Diabetes Prediction using Machine Learning

Agarwal, V., Kumar, S., Shukla, A., Singh, K., & Siddiqui, H. (2023) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n60023.2023.10541838 · Cited by 7

Multi-parametric Classification of Traumatic Brain Injury Patients Using Automatic Analysis of Quantitative MRI Scans

Aribisala, B. S., Cowie, C. J. A., He, J., Wood, J., Mendelow, A. D., Mitchell, P. D., & Blamire, A. M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15699-1_6 · Cited by 7

On the interpretability of the SVM model for predicting infant mortality in Bangladesh

Sayeed, M. A., Rahman, A., Rahman, A., & Rois, R. (2024) · Journal of Health Population and Nutrition · doi:10.1186/s41043-024-00646-9 · Cited by 6

Diabetes Diagnosis in Population by Intelligible Machine Learning

Vidushi, Rajak, A., & Shrivastava, A. K. (2020) · International Journal of Current Research and Review · doi:10.31782/ijcrr.2020.122415 · Cited by 6

RFFE – Random Forest Fuzzy Entropy for the classification of Diabetes Mellitus

Ruby, A. U., Chandran, J. G. C., J, S. J. T., Chaithanya, B. S. S., & Patil, R. (2023) · AIMS Public Health · doi:10.3934/publichealth.2023030 · Cited by 6

Machine Learning Model for Predicting Mortality Risk in Patients With Complex Chronic Conditions: Retrospective Analysis

Guillamet, G. H., Pallaruelo, A. N. M., Mezquita, L. M., Miralles, R., Mas, M. À., Ulldemolins, M., Estrada, O., & Seguí, F. L. (2023) · Online Journal of Public Health Informatics · doi:10.2196/52782 · Cited by 9

Ambient Intelligence (AmI)

Chen, T. (2024) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-031-54935-9_1 · Cited by 6

From data to insights

Khanam, A., Masoodi, F. S., & Bamhdi, A. M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-24001-0.00007-5 · Cited by 5

Role of artificial intelligence in cancer detection using protein p53: A Review

Patil, M. R., & Bihari, A. (2024) · Molecular Biology Reports · doi:10.1007/s11033-024-10051-4 · Cited by 6

Predicting Insulin Resistance in Children Using a Machine-Learning-Based Clinical Decision Support System

Hall, A. J., Hussain, A., & Shaikh, M. G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49685-6_25 · Cited by 5

Early Diabetes Prediction using Random Forest

Shrivastava, A. K., Karthikeyan, V., Kaushik, S., & Sudagar, M. (2022) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc54411.2022.9885683 · Cited by 5

Analysis And Comparison Of Diabetic Prediction Using Medium KNN Classifier And Cosine KNN Classifier

Nirupama, S., & Jenila Rani, D. (2022) · Journal of Pharmaceutical Negative Results · doi:10.47750/pnr.2022.13.s04.043 · Cited by 5

Type-2 diabetes identification from toe-photoplethysmography using Fourier decomposition method

Mishra, B., Nirala, N., & Singh, B. K. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09208-2 · Cited by 5

Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches

Hosseini, N., Tanzadehpanah, H., Mansoori, A., Sabzekar, M., Ferns, G. A., Esmaily, H., & Ghayour‐Mobarhan, M. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02887-y · Cited by 5

One-year renal graft survival prediction using a weighted decision tree classifier

Atallah, D. M., Eldesoky, A., Amira, H. F., & Ghoneim, M. A. (2014) · International Journal of Engineering & Technology · doi:10.14419/ijet.v3i3.2334 · Cited by 5

An Intelligent Type-II Diabetes Mellitus Diagnosis Approach using Improved FP-growth with Hybrid Classifier Based Arm

Vembandasamy, K., Karthikeyan, T., & Raghavan, B. (2015) · Research Journal of Applied Sciences Engineering and Technology · doi:10.19026/rjaset.11.1860 · Cited by 5

Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic Using Optimized EKF-RBFN Trained Prototypes

Adegoke, V. F., Chen, D., Banissi, E. K., & Barsikzai, S. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-17065-3_6 · Cited by 5

The SMART Framework: Selection of Machine Learning Algorithms With ReplicaTions—A Case Study on the Microvascular Complications of Diabetes

Swan, B. P., Mayorga, M. E., & Ivy, J. S. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3094777 · Cited by 5

Diabetes Disease Diagnosis Using Machine Learning Approach

Goyal, S., Batra, N., & Chhabra, K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2821-5_19 · Cited by 5

Practice of the new supervised machine learning predictive analytics for glioma patient survival after tumor resection: Experiences in a high-volume Chinese center

Li, Y., Ye, M., Jia, B., Chen, L., & Zhou, Z. (2023) · Frontiers in Surgery · doi:10.3389/fsurg.2022.975022 · Cited by 5

On Diabetes Classification and Prediction using Artificial Neural Networks

Diab, M. S., Husain, S., & Jarndal, A. (2020) · International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) · doi:10.1109/ccci49893.2020.9256621 · Cited by 5

Predicting Hypoglycemia in Diabetic Patients using Machine Learning Techniques

Eljil, K. A. A. S. (2014) · DSpace at AUS (American University of Sharjah) · doi:10.82366/377 · Cited by 5

Machine learning-based early detection of diabetes risk factors for improved health management

Nuthakki, P., & Kumar, T. P. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18728-5 · Cited by 4

Advances in Machine Learning Models for Healthcare Applications: A Precise and Patient-Centric Approach

Parashar, B., Sridhar, S. B., Kalpana, K., Malviya, R., Prajapati, B. G., & Uniyal, P. (2025) · Current Pharmaceutical Design · doi:10.2174/0113816128353371250119121315 · Cited by 4

Feature Assessment and Classification of Diabetes Employing Concept Lattice

Hao, J., Zheng, Y., Xu, C., Yan, Z., & Li, H. (2019) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2019.8791870 · Cited by 4

Diabetes Disease Prediction Using Classification Algorithms

Sangien, T., Bhat, T., & Khan, M. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-7637-6_17 · Cited by 4

Machine Learning Techniques for Diabetes Prediction: A Comparative Analysis

Abdelhafez, H. A. (2024) · Journal of Applied Data Sciences · doi:10.47738/jads.v5i2.219 · Cited by 4

Detection and Analysis of Life Style based Diseases in Early Phase of Life: A Survey

Kunekar, P. R., Gupta, M. K., & Agarwal, B. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-8300-7_6 · Cited by 4

Comparative Analysis of Machine Learning Algorithms for Histopathological Images of Oral Cancer

Panigrahi, S., Nanda, B. S., & Swarnkar, T. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-4807-6_31 · Cited by 4

Classification vs Clustering: Ways for Diabetes Detection

Singh, S., & Vazirani, V. (2022) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct54291.2022.9825053 · Cited by 4

A Multilayer Hybrid Machine Learning Model for Diabetes Detection

Parab, S., Rathod, P., Patil, D., & Chikkareddi, V. (2020) · ITM Web of Conferences · doi:10.1051/itmconf/20203203032 · Cited by 4

Medical Case Based Reasoning Frameworks

El–Sappagh, S., & Elmogy, M. M. (2019) · IGI Global eBooks · doi:10.4018/978-1-5225-9863-3.ch025 · Cited by 4

A machine learning-based clinical prediction rule for adverse outcomes in multimorbid patients

García-Luque, R., Pimentel, E., Durán, F., Aranda‐Gallardo, M., & Morales‐Asencio, J. M. (2025) · Intelligent Medicine · doi:10.1016/j.imed.2025.08.006 · Cited by 3

Mobile systems for healthcare 5.0: Applications, challenges and opportunities

Zhao, Y., Zuo, L., Yu, D., Pan, R., Fu, L., Lu, Y., & Zhang, H. (2026) · High-Confidence Computing · doi:10.1016/j.hcc.2026.100392 · Cited by 4

Non-destructive classification of type 2 diabetic teeth using LIBS-derived elemental biomarkers and machine learning

Dastageer, M. M., Siraj, K., Senesi, N., Israr, M. A., & Haq, S. U. (2026) · Analytica Chimica Acta · doi:10.1016/j.aca.2026.345615 · Cited by 3

Ensemble Machine Learning Algorithm for Diabetes Prediction in Maiduguri, Borno State

Dada, E. G., Birma, A. I., & Gora, A. A. (2024) · Mikailalsys Journal of Mathematics and Statistics · doi:10.58578/mjms.v2i2.2875 · Cited by 3

A Machine Learning Approach for Diagnosis of Diabetes Through Fusion of PPG and ECG Signals

Basit, H. A., & Faraz, M. (2024) · International Conference on Emerging Technologies · doi:10.1109/icet63392.2024.10935215 · Cited by 3

Hybrid Online Model for Predicting Diabetes Mellitus

Mallika, C., & Selvamuthukumaran, S. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.020543 · Cited by 3

Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine

Khan, M. F., Kalyan, G., Chakrabarty, S., & Mursaleen, M. (2022) · Nutrients · doi:10.3390/nu14142794 · Cited by 3

Machine learning-based predictive model for type 2 diabetes mellitus using genetic and clinical data

Huang, H. Y. R., Khan, A., Parikh, C., Basit, J., Saeed, S., Nair, A., Mehta, A., & Tse, G. M.-K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95686-4.00013-7 · Cited by 3

A systematic review on intelligent diagnosis of diabetes using rule-based machine learning techniques

Zhang, W., Khalid, S. G., Sadiq, S., Liu, H., & Wong, J. Y. H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95686-4.00001-0 · Cited by 3

A Comparative Study on Epileptic Seizure Detection Methods

Hadipour, S., Tokhmpash, A., & Shafai, B. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-51041-1_1 · Cited by 3

Early Prediction Model for Type-2 Diabetes Based on Lifestyle

Hirnak, K., Chaudhari, N., Singh, A. K., & Patil, D. (2020) · ITM Web of Conferences · doi:10.1051/itmconf/20203203053 · Cited by 3

Diabetes Prediction Using Different Ensemble Learning Classifiers in Machine Learning

Kumar, P. M., Haswanth, K. V. S., Swaroop, G. M., & Priyadarsini, M. J. P. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.46567 · Cited by 3

Diagnosis of diabetes by using Adaptive SVM

Gürbüz, E., & Kılıç, E. (2011) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2011.5929583 · Cited by 3

Selecting a computational classifier to develop a clinical decision support system (CDSS)

Maldonado, H., Leija, L., & Vera, A. (2015) · International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) · doi:10.1109/iceee.2015.7357960 · Cited by 3

Diagnosis of Metabolic Syndrome: A Diversity Based Hybrid Model

Barakat, N. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-41920-6_14 · Cited by 3

Assessing the Importance of Attributes for Diagnosis of Diabetes Disease

Akyol, K. (2017) · International Journal of Information Engineering and Electronic Business · doi:10.5815/ijieeb.2017.05.01 · Cited by 3

Machine Learning and Deep Learning Algorithms in the Diagnosis of Chronic Diseases

Battineni, G. (2021) · Studies in computational intelligence · doi:10.1007/978-981-16-0935-0_7 · Cited by 5

A Powerful Holonic and Multi-Agent-Based Front-End for Medical Diagnostics Systems

Akbari, Z., & Unland, R. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-79161-2_13 · Cited by 3

Personal Health Systems for Diabetes Management, Early Diagnosis and Prevention

Zarkogianni, K., & Nikita, K. S. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-6198-9.ch012 · Cited by 3

Performance Analysis of Machine Learning Techniques to Predict Diabetes MellitusPreprint

Faruque, M. F., Asaduzzaman, A., & Sarker, I. H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.10028 · Cited by 3

Evolutionary computation technique combined with ensemble model for classification of diabetes

Kumar, K. S., Suryakanth, G. V., & Giridhar, K. (2021) · African journal of diabetes medicine/The African journal of diabetes medicine · doi:10.54931/2053-4787.29-5-1 · Cited by 2

Automated detection of cervical spondylotic myelopathy: harnessing the power of natural language processing

Ren, G., Wang, P., Wang, Z., Xie, Z., Liu, L., Wang, Y., & Wu, X. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1421792 · Cited by 2

Machine learning algorithms-based decision support model for diabetes

Karthick, K., Manikandan, R., & Kumar, T. S. (2024) · Review of Computer Engineering Research · doi:10.18488/76.v11i1.3598 · Cited by 3

Precision in Prediction: A Comprehensive Assessment of Diabetes Forecasting through Machine Learning Techniques

Bajaj, P., Kanti, M., & Kumar, P. (2024) · International Conference on Smart Devices (ICSD) · doi:10.1109/icsd60021.2024.10751133 · Cited by 2

Prediction of Sudden Cardiac Arrest Due to Diabetes Mellitus Using Fuzzy Based Classification Approach

Devi, K. G. R. R., & Chozhan, R. M. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-43192-1_46 · Cited by 2

DS21, a new noninvasive technology, is effective and safe for screening for prediabetes and diabetes in Chinese population

Zhu, X., Tang, J. P., Lin, H., Chang, X., Xia, M., Wang, L., Yan, H., Bian, H., & Gao, X. (2020) · BioMedical Engineering OnLine · doi:10.1186/s12938-020-00823-x · Cited by 2

Predicting Diabetes Mellitus and its Complications through a Graph-Based Risk Scoring System

Erandathi, M., Wang, W. Y. C., & Mayo, M. (2020) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3418094.3418115 · Cited by 3

A Fuzzy Expert System & Neuro-Fuzzy System Using Soft Computing For Gestational Diabetes Mellitus Diagnosis

Maryam, M. Z., Mahmoud, A., & Ashkan, N. A. (2014) · Cited by 2

Evaluation of Intelligent System to the Control of Diabetes

Chan, H.-C., Chen, J., Chien, S.-W., Chen, Y.-F., & Bau, C.-T. (2012) · International Symposium on Computer, Consumer and Control · doi:10.1109/is3c.2012.153 · Cited by 2

Study of Data Mining Algorithms in the Context of Performance Enhancement of Classification

Goel, A., & Kr., S. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016907963 · Cited by 2

Multilayer machine learning algorithm to classify diabetic type on knee dataset

Anjaneya, L. H., & Holi, M. S. (2016) · IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology · doi:10.1109/rteict.2016.7807889 · Cited by 2

Application of Data Mining Techniques in Clinical Decision Making: A Literature Review and Classification

Ameri, H., Alizadeh, S., & Noughabi, E. A. Z. (2017) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-5225-2515-8.ch012 · Cited by 1

Model-driven Classification of Different Diabetes Types within a Personalized Diabetes Management

Tolks, C., Ament, C., & Eberle, C. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513641 · Cited by 2

Improving Diagnosis Accuracy of Diabetic Disease Using Radial Basis Function Network and Fuzzy Clustering

Hosseini, H., & Bardsiri, A. K. (2019) · Frontiers in Health Informatics · doi:10.30699/fhi.v8i1.203 · Cited by 2

Prediction and Analysis of Diabetes Mellitus on Different Features using Machine Learning Algorithm

Nayak, A. A. (2020) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2020.6138 · Cited by 2

Performance evaluation of machine learning classification techniques for Diabetes disease

Emmanuel, G., Hungilo, G. G., & Emanuel, A. W. R. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1098/5/052082 · Cited by 2

A Multi-Stage Approach Combining Feature Selection with Machine Learning Techniques for Higher Prediction Reliability and Accuracy in Heart Disease Diagnosis

Chaudhuri, A. K., Banerjee, D. K., Das, A., & Ray, A. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.18592092 · Cited by 2

Context Reasoning for Predicting Gestational Diabetes Mellitus Using CA-RETE Algorithm

Deeba, K., & Saravanaguru, R. A. K. (2021) · International Journal of e-Collaboration · doi:10.4018/ijec.2021100104 · Cited by 2

Apache Spark ile Makine Öğrenmesi Destekli Diyabet Rahatsızlığı Tahmini

Yıldırım, E., & Çalhan, A. (2022) · Düzce Üniversitesi Bilim ve Teknoloji Dergisi · doi:10.29130/dubited.999048 · Cited by 2

A Novel Approach to Predict Kidney Detection Using Support Vector Machine

Sharma, N., & Dalwal, S. (2019) · International Journal of Science and Research (IJSR) · doi:10.21275/19031902 · Cited by 2

Diabetes Prediction Using Machine Learning Algorithms and Hyperparameter Tuning for Expecting Mothers

Kumar, D. M., Deny, J., Nagaraj, P., Dhanveer, K., Kumar, V. P., & Arthi, R. (2023) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic57670.2023.10263941 · Cited by 2

A Brief View on Medical Diagnosis Applications with Deep Learning

Köse, U., Deperlioğlu, Ö., Alzubi, J. A. A., & Pătruţ, B. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-6325-6_3 · Cited by 2

Prediction and Management of Diabetes using Machine Learning: A Review

Islam, N. U. (2019) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2019.5233 · Cited by 1

Privacy-preserving medical diagnosis system with Gaussian kernel-based support vector machine

Wu, R., Wang, B., & Zhao, Z. W. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-024-01743-6 · Cited by 1

An Integrated Machine Learning Framework for Early Detection of Chronic Kidney Disease

Prakash, A., Priyadarshi, M., Chaurasia, J., & Jamwal, A. (2025) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon66527.2025.11280929 · Cited by 1

Mobile data acquisition towards contextual risk assessment for better disease management in diabetes

Cernăzanu-Glăvan, C., Lungeanu, D., & Holban, Ş. (2014) · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) · doi:10.1109/saci.2014.6840088 · Cited by 1

Toward Prevention of Adverse Events Using Anticipatory Analytics

Norman, J., Akhavan, A., Chen, S., Aron, D. C., Leykum, L. K., & Yaneer Bar-yam, A. (2020) · Progress in Preventive Medicine · doi:10.1097/pp9.0000000000000029 · Cited by 1

Artificial Intelligence for Medical Diagnosis

Richens, J. G., & Buchard, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_29-1 · Cited by 1

Predicción de diabetes mellitus tipo 2 utilizando atributos médicos del Policlínico Leo SAC de San Juan de Lurigancho mediante el enfoque de Machine Learning

Malpartida, J. Y. R. (2022) · TecnoHumanismo · doi:10.53673/th.v2i4.123 · Cited by 1

Predicting Hypoglycemia in Diabetic Patients Using Time-Sensitive Artificial Neural Networks

Eljil, K. S., Qadah, G. Z., & Pasquier, M. (2019) · IGI Global eBooks · doi:10.4018/978-1-7998-0414-7.ch069 · Cited by 1

Prediction of Heart Disease at an Early Phase Using Machine Learning Techniques

Vijayarangan., R., Hemalatha, M., Umadevi., K., & Satishkumar., R. (2022) · International Conference on Data Science and Information System (ICDSIS) · doi:10.1109/icdsis55133.2022.9915987 · Cited by 1

Analysis of the Performance of Data Mining Classification Algorithm for Diabetes Prediction

Sajwan, V., Awasthi, M., Awasthi, P., Goel, A., Khanduja, M., & Kumar, A. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-22915-2_3 · Cited by 1

Diagnosis of Diabetes Mellitus using Pulse Plethysmograph

Raza, M., Arshad, A., Faraz, M., Aziz, S., Khan, M. U., & Imtiaz, M. A. (2022) · International Conference on IT and Industrial Technologies (ICIT) · doi:10.1109/icit56493.2022.9988989 · Cited by 1

An Empirical Comparison of Classification Machine Learning Models Using Medical Datasets

Rama, B. V. S., Suryanarayana, G., Ansari, M. D., & Begum, R. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-2058-7_29 · Cited by 1

Diadisc: A Machine Learning Approach for Classification and Prediction of Diabetes Mellitus

Angel, T. S. S., Kumar, G. S., Nomula, V. N. S. R., Bommerla, A., Vijayakumar, K., & Snehalatha, N. (2023) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai59313.2023.10452633 · Cited by 1

LDA-GWO: Utilizing LDA and Gray Wolf Optimizer in Diabetes Diseases Diagnosis

Khosravi, A., & Azarnik, A. (2024) · CSI International Symposium on Artificial Intelligence and Signal Processing · doi:10.1109/aisp61396.2024.10475253 · Cited by 1

Extracting Diagnostic Rules from Support Vector Machine

A, G. S., & B, D. D. (2013)

Rule Extraction by Support Vector Machine: A Case Study of Type II Diabetes Mellitus Diagnosis

Yang, C.-H., Yeh, T., & Liao, M. (2009) · IFAC Proceedings Volumes · doi:10.3182/20090603-3-ru-2001.00280

Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes

Han, L., Luo, S., Yu, J., Pan, L., & Chen, S. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2325615 · Cited by 135

Prediction and Diagnosis of Diabetes Mellitus Classes in Humans using Support Vector Machine

Sankarganesh, P. V., & Priya, R. (2025) · International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE) · doi:10.1109/iccrtee64519.2025.11052987

Diabetes Mellitus Detection using Support Vector Machine

Khan, Q. R., & Asger, M. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.21569891