Cited by 242Artificial Intelligence in HealthcareAI in cancer detectionData Mining and Machine Learning Applications

The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.

Decision tree classifiers for automated medical diagnosis

Azar, Ahmad Taher, El-Metwally, Shereen M.·2013·Neural Computing and Applications

DOI 10.1007/s00521-012-1196-7

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/s00521-012-1196-7

Cites

86 of 86 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
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
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
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
Bagging predictors
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
Experiments with a new boosting algorithm
Freund, Y., & Schapire, R. E. (1996) · Cited by 8,189

Flagged first, then by citations

Show all 86 references

Research works printed but not linked: 4

Endo A, Shibata T, Tanaka H (2008) Comparison of seven algorithms to predict breast cancer survival. Biomed Soft Comput Hum Sci 13(2):11–16
Ho T (1995) Random decision forest. In: 3rd international conference on document analysis and recognition, Montreal, Canada, August 14–18, 1995, pp 278–282
Shanthi S, Bhaskaran VM (2011) Intuitionistic fuzzy C-means and decision tree approach for breast cancer detection and classification. Eur J Sci Res 66(3):345–351
Theodoridis S, Koutroumbas K (2006) Pattern recognition, 3rd edn. Academic Press, San Diego

Sorted by citations

Items 101–200 of 240

Short Term Electricity Load Forecasting Through Machine Learning
Azar, A. T., Khamis, A. M., Kamal, N. A., & Galli, B. J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-44289-7_40 · Cited by 9
Self-citation
Digital Forensic Science
Shetty, B. S. K., & Shetty, K. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.78450 · Cited by 9
Treebeard: An Optimizing Compiler for Decision Tree Based ML Inference
Prasad, A., Rajendra, S., Rajan, K., Govindarajan, R., & Bondhugula, U. K. R. (2022) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro56248.2022.00043 · Cited by 8
LAMIS-DMDB: A new full field digital mammography database for breast cancer AI-CAD researches
Imane, O., Amroune, M., Lazhar, R. F., Soltani, H., Elhadj, B., & Conci, A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105823 · Cited by 8
A comparative performance assessment of artificial intelligence based classifiers and optimized feature reduction technique for breast cancer diagnosis
Batool, S., & Zainab, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109215 · Cited by 8
A Contrastive Study of Machine Learning on Energy Firm Value Prediction
Zhang, C., Zhang, H., & Liu, D. (2019) · IEEE Access · doi:10.1109/access.2019.2953807 · Cited by 8
A Machine Learning Approach to Predict Breast Cancer Using Boosting Classifiers
Rahman, M. M., Ferdousi, Z., Saha, P., & Mayuri, R. A. (2023) · Indian Journal of Computer Science and Engineering · doi:10.21817/indjcse/2023/v14i3/231403009 · Cited by 8
Computer Aided Intelligent Breast Cancer Detection: Second Opinion for Radiologists—A Prospective Study
Dheeba, J., & Singh, N. (2014) · Studies in computational intelligence · doi:10.1007/978-3-319-11017-2_16 · Cited by 8
Boosted Decision Trees for Vertebral Column Disease Diagnosis
Azar, A. T., Ali, H. S., Bălaş, V. E., Olariu, T., & Ciurea, R. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-18296-4_27 · Cited by 8
Self-citation
Secure and Fast Decision Tree Evaluation on Outsourced Cloud Data
Liu, L., Su, J., Chen, R., Chen, J., Sun, G., & Li, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30619-9_26 · Cited by 8
Decision Tree Model of Smoking Behaviour
Abo-Tabik, M., Costen, N. P., Darby, J., & Benn, Y. (2019) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00311 · Cited by 8
SilvanForge: A Schedule-Guided Retargetable Compiler for Decision Tree Inference
Prasad, A., Rajendra, S., Rajan, K., Govindarajan, R., & Bondhugula, U. K. R. (2024) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3694715.3695958 · Cited by 7
Interpretable mango leaf disease detection using a hybrid CNN–transformer model with GLCM features
Jashim, F. B., Refat, F. R., Karim, M. H., Mahmud, F. U., & Siddiqui, M. I. H. (2025) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2025.15.2.1510 · Cited by 7
Biomarker discovery for early breast cancer diagnosis using machine learning on transcriptomic data for biosensor development
Mayoral-Peña, K., Peña, O. I. G., Artzi, N., & de Donato, M. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110584 · Cited by 7
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
Hybrid Rough Set With Black Hole Optimization-Based Feature Selection Algorithm for Protein Structure Prediction
Inbarani, H. H., Azar, A. T., Azar, A. T., & Bagyamathi, M. (2021) · International Journal of Sociotechnology and Knowledge Development · doi:10.4018/ijskd.290657 · Cited by 7
Self-citation
From Evaluation to Prediction: Analysis of Diabetic Autonomic Neuropathy Using Sudoscan and Artificial Intelligence
Toderean, R., Cobuz, M., Dimian, M., & Cobuz, C. (2024) · Applied Sciences · doi:10.3390/app14167406 · Cited by 7
Machine Learning Model for Predicting Walking Ability in Lower Limb Amputees
Knežević, A., Arsenovic, J., Garipi, E., Platisa, N., Savic, A., Aleksandrić, T., Popović, D., Subic, L., Milenović, N., Simić-Panić, D., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13226763 · Cited by 7
A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification
Huynh, P.-H., & Nguyen, V. H. (2023) · Engineering innovations · doi:10.4028/p-h0cef4 · Cited by 7
An improved decision tree algorithm based on mutual information
Fang, L., Jiang, H., & Cui, S. (2017) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2017.8393008 · Cited by 7
A comprehensive study of upward fuzzy preference relation based fuzzy rough set models: Properties and applications in treatment of coronavirus disease
Rehman, N., Ali, A., Лю, П., & Hila, K. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22433 · Cited by 7
IDDSAM: An Integrated Disease Diagnosis and Severity Assessment Model for Intensive Care Units
Shi, Z., Zuo, W., Liang, S., Zuo, X., Yue, L., & Li, X. (2020) · IEEE Access · doi:10.1109/access.2020.2967417 · Cited by 9
Rapid identification of adulteration in American ginseng powder using near-infrared spectroscopy combined with machine learning
Zhai, D., Zhou, J., Ma, J., Liu, A., Liu, J., Meng, Z., Qiu, C., Yang, Y., Zhang, N., & Li, P. (2025) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2025.127409 · Cited by 6
Feature Extraction and Feature Selection Procedures for Medical Image Analysis
Gupta, S. S., & Gupta, S. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-5226-7.ch010 · Cited by 6
A Massively Parallel SMC Sampler for Decision Trees
Drousiotis, E., Varsi, A., Phillips, A. M., Maskell, S. R., & Spirakis, P. G. (2025) · Algorithms · doi:10.3390/a18010014 · Cited by 6
Neural network fast‐classifies biological images through features selecting to power automated microscopy
Balluet, M., Sizaire, F., Habouz, Y. E., Walter, T., Pont, J., Giroux, B., Bouchareb, O., Tramier, M., & Pécréaux, J. (2021) · Journal of Microscopy · doi:10.1111/jmi.13062 · Cited by 6
On multivariate randomized classification trees: l0 -based sparsity, VC dimension and decomposition methods
Amaldi, E., Consolo, A., & Manno, A. (2022) · Computers & Operations Research · doi:10.1016/j.cor.2022.106058 · Cited by 6
Proposing a Business Model in Healthcare Industry
Hajiheydari, N., Khakbaz, S. B., & Farhadi, H. (2013) · International Journal of Healthcare Information Systems and Informatics · doi:10.4018/jhisi.2013040104 · Cited by 6
Machine Learning Techniques for Handwritten Digit Recognition
Azar, A. T., Khamis, A. M., Kamal, N. A., & Galli, B. J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-44289-7_39 · Cited by 6
Self-citation
RWD-derived response in multiple myeloma
Xu, T., Roose, J. P., Williamson, M., Sawas, A., Hong, W., Jin, H., Maignan, K., Rocci, A., Yousefi, K., Kumar, S. K., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0285125 · Cited by 5
Enhancing Particulate Matter Risk Assessment with Novel Machine Learning-Driven Toxicity Threshold Prediction
Jairi, I., Rekbi, A., Othman, S. B., Hammadi, S., Canivet, L., & Zgaya, H. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109531 · Cited by 7
A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture
Kızıloluk, S., Karaduman, M., Aslan, S., Yıldırım, M., Khan, M. A., Alhayan, F., & Nam, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-00642-x · Cited by 5
Machine Learning-Based Predictive Model for Risk Stratification of Multiple Myeloma from Monoclonal Gammopathy of Undetermined Significance
Santamaría, A., Alfaro, M., Rodríguez, C. A., Sánchez-Quiñones, B., Ibarra, N., Gil, A., Reinoso, Ó., & Payá, L. (2025) · Electronics · doi:10.3390/electronics14153014 · Cited by 5
The Power of Adaptivity for Stochastic Submodular Cover
Ghuge, R., Gupta, A., & Nagarajan, V. (2022) · Operations Research · doi:10.1287/opre.2022.2388 · Cited by 5
Retracted: Automatic Breast Tumor Cancer Diagnosis Based on a Hybrid Densenet
Jayandhi, G., Jasmine, J. S. L., Seetharaman, R., Joans, S. M., & Joy, R. P. (2022) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs54159.2022.9784979 · Cited by 5
Retracted
Comparative Study of Machine Learning Models on Multiple Breast Cancer Datasets
Sujon, M. A. H., & Mustafa, H. A. (2023) · International Journal of Advanced Science Computing and Engineering · doi:10.62527/ijasce.5.1.105 · Cited by 5
Consent-based Privacy-preserving Decision Tree Evaluation
Xue, L., Liu, D., Ni, J., Lin, X., & Shen, X. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9149181 · Cited by 5
The Perspectives of Medical Errors in the Health Care Industry
Kasemsap, K. (2017) · Advances in medical education, research, and ethics (AMERE) book series · doi:10.4018/978-1-5225-2337-6.ch005 · Cited by 5
Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View
VanBerlo, B., Smith, D., Tschirhart, J. N., VanBerlo, B., Wu, D., Ford, A., McCauley, J., Wu, B., Chaudhary, R., Dave, C., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12102351 · Cited by 5
Translational Applications of Machine Learning in Auditory Electrophysiology
Smith, S. B. (2022) · Seminars in Hearing · doi:10.1055/s-0042-1756166 · Cited by 5
A Comparative Analysis of Tree-based Machine Learning Algorithms for Breast Cancer Detection
A’la, F. Y., Permanasari, A. E., & Setiawan, N. A. (2019) · International Conference on Information & Communication Technology and System (ICTS) · doi:10.1109/icts.2019.8850975 · Cited by 5
Axiomatic fuzzy set theory-based fuzzy oblique decision tree with dynamic mining fuzzy rules
Cai, Y., Zhang, H., Sun, S., Wang, X., & He, Q. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04649-0 · Cited by 5
A neural network approach for the prediction of arrhythmic events in patients with Brugada syndrome via ECG features analysis
Caligari, S., Randazzo, V., Gaïta, F., Giustetto, C., Millesimo, M., & Pasero, E. G. A. (2024) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon56669.2024.10608666 · Cited by 4
Unlocking the Potential: Machine Learning and Deep Learning in Leukemia Diagnosis with Explainable AI
Deshpande, N. M., Gite, S., & Pradhan, B. (2024) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-031-68602-3_12 · Cited by 4
Breast invasive ductal carcinoma diagnosis using machine learning models and Gabor filter method of histology images
Kadhim, R. R., & Kamil, M. Y. (2023) · International Journal of Reconfigurable and Embedded Systems (IJRES) · doi:10.11591/ijres.v12.i1.pp9-18 · Cited by 4
Predicting ICU Mortality Based on Generative Adversarial Nets and Ensemble Methods
Wei, M., Huang, Z., Diping, Y., & Yang, L. (2023) · IEEE Access · doi:10.1109/access.2023.3296147 · Cited by 4
Investigating the spatial foundations of rural entrepreneurship development using a hybrid method of MCDM, ANN and DTree algorithm
Ye, D., Dehrashid, H. A., Moayedi, H., & Dehrashid, A. A. (2024) · Environment Development and Sustainability · doi:10.1007/s10668-024-04739-7 · Cited by 4
A Random Forest-based Automatic Classification of Dental Types and Pathologies using Panoramic Radiography Images
Singh, S. B., Laishram, A., Thongam, K., & Singh, K. M. (2024) · Journal of Scientific & Industrial Research · doi:10.56042/jsir.v83i5.3994 · Cited by 4
Assessment of Albumin-Incorporating Scores at Hepatocellular Carcinoma Diagnosis Using Machine Learning Techniques: An Evaluation of Prognostic Relevance
Suárez, M., Martínez-Blanco, P., Gil-Rojas, S., Torres, A., Torralba, M., & Mateo, J. (2024) · Bioengineering · doi:10.3390/bioengineering11080762 · Cited by 4
Predicting Factors Associated With Uncontrolled Hypertension Using Machine Learning Methods: A Cross‐Sectional Study in Western Iran
Cheraghi, Z., Doosti-Irani, M., Sohrabi, M., & Doosti‐Irani, A. (2025) · International Journal of Hypertension · doi:10.1155/ijhy/4011397 · Cited by 4
State-of-the-Art Machine Learning and Deep Learning Techniques for Parking Space Classification: A Systematic Review
Navpreet, Rani, R., & Roul, R. K. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10250-7 · Cited by 4
GTree: GPU-friendly Privacy-preserving Decision Tree Training and Inference
Wang, Q., Cui, S., Zhou, L., Ye, D., Bai, J., Koh, Y. S., & Russello, G. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00118 · Cited by 4
Certain investigations on prenatal medical image analysis
Dharshini, K. P., Ramya, R. S., & Srinivasan, K. (2017) · IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE) · doi:10.1109/iceice.2017.8191912 · Cited by 4
Analysis of micro(nano)plastics based on automated data interpretation and modeling: A review
Ko, K., Lee, J., Baumann, P., Kim, J., & Chung, H. (2024) · NanoImpact · doi:10.1016/j.impact.2024.100509 · Cited by 4
The Intelligent Diagnostic System for Common Diseases using the Optimized Medical Knowledge Graph
Wang, J., Liu, N., Hu, Q., Wu, Z., Dong, W., & Shen, Y. (2020) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc48107.2020.9148406 · Cited by 4
Generalized multigranulation fuzzy rough sets based on upward additive consistency
Rehman, N., & Ali, A. (2021) · Soft Computing · doi:10.1007/s00500-020-05491-6 · Cited by 4
A scaling up approach: a research agenda for medical imaging analysis with applications in deep learning
Afriyie, Y., Weyori, B. A., & Opoku, A. A. (2023) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2023.2165721 · Cited by 4
DMMAM: Deep Multi-source Multi-task Attention Model for Intensive Care Unit Diagnosis
Shi, Z., Zuo, W., Chen, W., Yue, L., Hao, Y., & Liang, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-18579-4_4 · Cited by 4
Ensemble Learning in the Presence of Noise
Sabzevari, M. (2019) · Biblos-e Archivo (Universidad Autónoma de Madrid) · Cited by 4
Estudos de técnicas de análise e tecnologias para o desenvolvimento de medidores inteligentes de energia residenciais
de Souza, W. A. (2016) · doi:10.47749/t/unicamp.2016.973459 · Cited by 4
Review on Anatomical Medical Images Classification Methods: Diagnostic Value
Yousif, A. S., Sheikh, U. U., Omar, Z., & Mahmood, O. A. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1804/1/012120 · Cited by 3
Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews
Andreadis, T., Gasteratos, A. C., Seimenis, I., & Koulouriotis, D. E. (2025) · Bioengineering · doi:10.3390/bioengineering12111160 · Cited by 3
An intelligent analytics framework for supplier selection and delay risk management in sustainable supply chains
Ziari, M., Soheyli, A., & Taleizadeh, A. A. (2026) · Supply Chain Analytics · doi:10.1016/j.sca.2026.100208 · Cited by 3
Retracted: Artificial Intelligence for Cancer Diagnosis and Prognosis: Current Status and Future Directions
Negi, A., Gupta, P., & Pradhan, R. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10308395 · Cited by 3
Retracted
Predicting Asthma Attacks Through AI-Powered Thermal Imaging Analysis of Breathing Patterns
Hamza, A., Himeur, Y., Amira, A., & Oulefki, A. (2024) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice62917.2024.00018 · Cited by 3
Women health issue: machine learning ensemble techniques to diagnosis breast cancer
Chaurasia, V., & Goel, N. (2024) · Health and Technology · doi:10.1007/s12553-024-00925-9 · Cited by 3
Interpretability of Deep Learning Analysis Result of Intradialytic Hypotension Prediction Model with Recommendation Reports Utilizing Large Language Model
Lin, Y., Wang, J., Lee, M.-H., & Hu, H. (2024) · IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) · doi:10.1109/ecbios61468.2024.10885460 · Cited by 3
An analog ReLu-based decision tree circuit architecture for biomedical applications
Alimisis, V., Moustakas, V., Cheliotis, K., Mylona, A., & Sotiriadis, P. P. (2025) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-025-02481-2 · Cited by 3
Managing Boundary Uncertainty in Diagnosing the Patients of Rural Area Using Fuzzy and Rough Set
Das, S., & Sil, J. (2022) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-021-00109-4 · Cited by 3
A Survey on Exploring Deep Learning in Medical Image Processing
Tripathi, A., & Goel, A. K. (2021) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n53548.2021.9725414 · Cited by 3
A cloud-based optimal fuzzy clustering of distributed data
Jbali, R. S., & Hidri, M. S. (2015) · International Journal of Intelligent Engineering Informatics · doi:10.1504/ijiei.2015.069099 · Cited by 3
Improving disease diagnosis by a new hybrid model
Sarkar, B. K. (2017) · New Horizons in Translational Medicine · doi:10.1016/j.nhtm.2017.07.001 · Cited by 3
Breaking new ground in cardiovascular heart disease Diagnosis K-RFC: An integrated learning approach with K-means clustering and Random Forest classifier
Osman, A. H., Ibrahim, A. O., Alsadoon, A., Alzahrani, A. A., Barukab, O., Abulfaraj, A. W., & Alharbi, N. M. (2024) · AIMS Mathematics · doi:10.3934/math.2024402 · Cited by 2
Crayfish-Optimized CNN and Random Forest for Effective Plant Disease Detection
Muthurajkumar, S., Kumar, G., & Priya, S. T. P. M. (2025) · Advances in environmental engineering and green technologies book series · doi:10.4018/979-8-3693-8019-2.ch007 · Cited by 2
Efficient mortality prediction for acute respiratory failure: a resource-constrained machine learning approach using MIMIC databases
Khan, M. T., Gulzar, M., Ali, A., Wali, A., & Amir, R. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11387-4 · Cited by 2
PDTE: Outsourced Private Decision Tree Evaluation for Large Trees With Sublinear Communication
Li, D. Q., He, K., Liu, J.-N., Yang, A., Liu, X., & Weng, J. (2026) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2026.3689639 · Cited by 2
Cognitive constraints in bilingual processing—an entropy-based discrimination between translation and second language production
Tang, C., Huang, D., & Cheung, A. K. F. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1503147 · Cited by 2
MPSE identifies newborns for whole genome sequencing within 48 h of NICU admission
Peterson, B. D., Juarez, E. F., Moore, B., Hernandez, E. J., Frise, E., Li, J., Lussier, Y. A., Tristani‐Firouzi, M., Reese, M. G., Jenkins, S. M., et al. (2025) · npj Genomic Medicine · doi:10.1038/s41525-025-00506-3 · Cited by 2
Sequential testing problem: A follow-up review
Ünlüyurt, T. (2025) · Discrete Applied Mathematics · doi:10.1016/j.dam.2025.07.039 · Cited by 2
Covering rough set-based classification for cardiac arrhythmia
Kumar, S. S., & Inbarani, H. H. (2017) · International Journal of Intelligent Engineering Informatics · doi:10.1504/ijiei.2017.084167 · Cited by 2
Performance Analysis of Some Competent Learners on Medical Data
Kumar, A., & Sarkar, B. K. (2020) · International Journal of Knowledge-Based Organizations · doi:10.4018/ijkbo.2021010103 · Cited by 2
Variational learning of finite shifted scaled Dirichlet mixture models
Arjmandiasl, Z., Manouchehri, N., Bouguila, N., & Bentahar, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822314-7.00012-2 · Cited by 2
Considering Currency in Decision Trees in the Context of Big Data
Hristova, D. (2014) · University of Regensburg Publication Server (University of Regensburg) · doi:10.5283/epub.31227 · Cited by 2
A Two-Stage Model for Breast Cancer Detection and Classification from Mammograms
Oladele, M. O., Adekiigbe, A., & Adepoju, T. M. (2022) · International Journal of Innovative Research and Development · doi:10.2139/ssrn.4068212 · Cited by 2
Reducing control packets using covering rough set for route selection in mobile ad hoc networks
Sudhakar, T., & Inbarani, H. H. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90527-5.00006-x · Cited by 2
Miscoding Alerts Within Hospital Datasets: An Unsupervised Machine Learning Approach
Souza, J., Santos, J. V., Lopes, F., Viana, J., & Freitas, A. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-77712-2_115 · Cited by 2
Expert System for Diagnosing Disease Risk from Urine Tests
Tachpetpaiboon, N., Kularbphettong, K., & Janpla, S. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-0344-9_20 · Cited by 2
Using convolutional neural networks for breast cancer diagnosing
Naderan, M., Zaychenko, Y. P., & Napoli, A. (2019) · System research and information technologies · doi:10.20535/srit.2308-8893.2019.4.09 · Cited by 3
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
Analysis of Hypertuning Based CNN Breast Cancer Predictor for Different Random States
Gupta, N., Gupta, H., & Surjeet (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.03.189 · Cited by 1
Wheel Slippage Detection Using IMU and Classification for a Wheeled Mobile Robot
Sibinović, V. D., Mitić, V. M., Jovanović, A., & Veselić, B. (2025) · International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) · doi:10.1109/icest66328.2025.11098286 · Cited by 1
Breast Cancer Detection Using New Ensemble Classifier Based on Deep Learning Methods
Palarimath, S. (2025) · International Conference on Intelligent Systems for Cybersecurity (ISCS) · doi:10.1109/iscs69371.2025.11386051 · Cited by 1
Applications of Machine Learning in Forensic Science
Vajpayee, K., Paida, V., Shah, S., Gandhi, Y., & Shukla, R. K. (2025) · Springer eBooks · doi:10.1007/978-981-96-8971-2_8 · Cited by 1
Introduction of Artificial Intelligence in Healthcare
Tayebi, L., Shetty, S. P., & Imani, S. (2026) · Springer eBooks · doi:10.1007/978-3-032-15977-9_1 · Cited by 1
Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique
Kiprop, S., Wamwea, C., Imboga, H., & Chelule, J. C. (2023) · American Journal of Theoretical and Applied Statistics · doi:10.11648/j.ajtas.20231205.14 · Cited by 1
A study on the crucial indicators for breast cancer detection using machine learning algorithm
Li, S. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2646/1/012042 · Cited by 1
ARTIFICIAL INTELLIGENCE IN TACKLING CORONAVIRUS AND FUTURE PANDEMICS
Quazi, S., Singh, S. K., Saha, R. P., Das, A., & Singh, M. K. (2024) · Journal of Experimental Biology and Agricultural Sciences · doi:10.18006/2024.12(1).124.137 · Cited by 1
Artificial Intelligence in Breast Cancer Diagnosis: A Review
Karampotsis, E., Panourgias, E. C., & Dounias, G. D. (2024) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-62316-5_2 · Cited by 1
Advanced Machine Learning Techniques for Improved Diagnostic Accuracy in Medical Imaging: A Novel Approach
Pandey, R. K., & Shukla, P. K. (2024) · International Conference on Cybernation and Computation (CYBERCOM) · doi:10.1109/cybercom63683.2024.10803179 · Cited by 1
Performance analysis of GA-based iterative and non-iterative learning approaches for medical domain data sets
Kumar, A., & Sarkar, B. K. (2017) · Intelligent Decision Technologies · doi:10.3233/idt-170298 · Cited by 1

Authors: Azar, A. T., & El-Metwally, S. M.
Affiliations: Misr University for Science and Technology (EG)
Cairo University (EG)
Corresponding author: Azar, Ahmad Taher
ORCID: Azar, Ahmad Taher
El-Metwally, Shereen M.
Venue: Neural Computing and Applications
Publisher: Springer Science and Business Media LLC
Published: 2012-10-07
Volume: 23
Issue: 7-8
Pages: 2387-2403
ISSN: 0941-0643 (ISSN-L)
1433-3058
DOI: 10.1007/s00521-012-1196-7
OpenAlex: W1982067074
Cited by: 242
References: 86
Citations by year: 2026: 9 · 2025: 29 · 2024: 29 · 2023: 27 · 2022: 26 · 2021: 24 · 2020: 28 · 2019: 18 · 2018: 15 · 2017: 19 · 2016: 8 · 2015: 6 · 2014: 3 · 2013: 1
FWCI: 5.20
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Neural Computing and Applications (publishedVersion)
Funders: National Cancer Institute
Topics: Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords: breast cancer detection, decision tree classification, boosted decision trees, decision tree forest, clinical decision support systems, automated medical diagnosis, Wisconsin Breast Cancer dataset, classifier comparison

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

Decision tree classifiers for automated medical diagnosis

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks