Predictive data mining in clinical medicine: Current issues and guidelines

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

Applied logistic regression

(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305

Support-Vector Networks

Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565

Statistical Learning Theory

Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

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

Cluster analysis and display of genome-wide expression patterns

Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855

Use of X means and C4.5 algorithms on lateral cephalometric measurements to identify craniofacial patterns

Gonca, M., & Özel, M. B. (2025) · BMC Oral Health · doi:10.1186/s12903-025-06651-6 · Cited by 1

Leveraging Large Language Models for Feature Selection in Drug Recommendation Systems

Dardour, S., & Yanes, N. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70370 · Cited by 4

Clinical Decision Support Through Machine Learning: Benchmarking LightGBM and Deep Learning Models for Diabetes Onset Prediction

Mittal, I., Jeya, J. S., Jain, V., Sharma, S., Patel, A., & Kohli, M. (2025) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic64647.2025.11330336 · Cited by 1

Predicting anemia treatment outcomes in maintenance hemodialysis patients using multiple machine learning models

Chen, M., Chen, M., Zhang, T., & Xie, L. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03544-8 · Cited by 1

Novel Machine Learning Techniques for Micro-Array Data Classification

El, N., Ahmed, E., & El, I. (2011) · InTech eBooks · doi:10.5772/22132 · Cited by 1

Selección supervisada de polimorfismos de nucleótido único en el síndrome de fatiga crónica

Cifuentes, R., & Barreto, E. (2011) · Biomédica · doi:10.7705/biomedica.v31i4.425 · Cited by 1

Data Mining for the Unique Identification of Patients in the National Healthcare Systems

Ramirez-Ríos, D. G., Romero, L. P. M., Páez-Logreira, H., Ramírez, L., & Florez, Y. A. J. (2015) · International Conference on Operations Research and Enterprise Systems · doi:10.5220/0005287302110217 · Cited by 1

An Effective Intelligent Model for Finding an Optimal Number of Different Pathological Types of Diseases

Mohamed, A. E.-R., Taha, E. T., & Nabil, M. A. (2011) · International Journal of Computer Applications · Cited by 1

Assessment of Cardiovascular Disease Risk Prediction Models: Evaluation Methods

Nayak, R., & Pitt, E. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20152-3_28 · Cited by 1

Collaborative data mining for clinical trial analytics

Gholap, J., Janeja, V. P., Yesha, Y., Chintalapati, R., Marwaha, H., & Modi, K. (2015) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2015.7359829 · Cited by 1

Multi-Criteria Decision Support Guided by Case-Based Reasoning

Mansoul, A., Atmani, B., & Benbelkacem, S. (2013) · doi:10.5121/csit.2013.3816 · Cited by 1

Cost-Sensitive Learning in Medicine

Freitas, A., Brazdil, P., & Costa‐Pereira, A. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-218-3.ch003 · Cited by 1

Overview of Predictive Modeling Approaches in Health Care Data Mining

Soni, S. (2014) · Advances in data mining and database management book series · doi:10.4018/978-1-4666-5063-3.ch015 · Cited by 1

Alcoholic Behavior Prediction through Comparative Analysis of J48 and Random Tree Classification Algorithms using WEKA

Kaur, N., & Singh, W. (2016) · Indian Journal of Science and Technology · doi:10.17485/ijst/2016/v9i32/100716 · Cited by 1

Pervasive Ensemble Data Mining Models to Predict Organ Failure and Patient Outcome in Intensive Medicine

Portela, F., Santos, M. F., Silva, Á., Abelha, A., & Machado, J. M. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-54105-6_27 · Cited by 1

Prediction and Diagnosis of Down Syndrome Disease by using the CHAID Algorithm

Saydali, S., & Parvïn, H. (2019) · Journal of Research in Science Engineering and Technology · doi:10.24200/jrset.vol3iss04pp31-34 · Cited by 1

Developing and testing models to predict mortality in the general population

Goldfarb‐Rumyantzev, A. S., Brown, R. S., Dong, N. C., Sandhu, G. S., Vohra, P., & Gautam, S. (2019) · Informatics for Health and Social Care · doi:10.1080/17538157.2019.1656209 · Cited by 1

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Raja, B. S., & Asghar, S. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110287 · Cited by 1

Identification of blood glucose patterns in patients with type 1 diabetes using continuous glucose monitoring and clustering technique

Contador, S., Botella-Serrano, M., Garnica, Ó., Cabo, J. M. V., Zurimendi, A. A., Martínez, R. R., Maqueda, E., & Pérez, J. I. H. (2021) · Endocrinología Diabetes y Nutrición (English ed ) · doi:10.1016/j.endien.2021.06.001 · Cited by 1

Cost-Sensitive Learning in Medicine

Freitas, A., Brazdil, P., & Costa‐Pereira, A. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-818-7.ch607 · Cited by 1

Overview of Predictive Modeling Approaches in Health Care Data Mining

Soni, S. (2015) · IGI Global eBooks · doi:10.4018/978-1-4666-9562-7.ch004 · Cited by 1

Knowledge Discovery and Data Mining Applications in the Healthcare Industry

Barazandeh, I., & Gholamian, M. R. (2015) · IGI Global eBooks · doi:10.4018/978-1-4666-8756-1.ch055 · Cited by 1

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

Walczak, S. (2019) · IGI Global eBooks · doi:10.4018/978-1-7998-1204-3.ch021 · Cited by 1

A Network-based Modeling Approach to Identify the Common Disease Classes in Patients with Late-stage Prostate Cancer

Jazayeri, A. R., Monselise, M., Jafari, N., Nikita, N., Yang, C. C., & Lu-Yao, G. L. (2022) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi54592.2022.00028 · Cited by 1

Prognostic Biomarkers to Predict Outcomes in Trauma

Bonaroti, J. W., Li, S. R., Abdelhamid, S. S., & Billiar, T. R. (2023) · Biomarkers in disease · doi:10.1007/978-3-031-07395-3_8 · Cited by 1

Medical Application of Big Data: Between Systematic Review and Randomized Controlled Trials

Shim, S. R., Lee, J.-H., & Kim, J. H. (2023) · Applied Sciences · doi:10.3390/app13169260 · Cited by 1

Prediction of cervical lymph node metastasis in papillary thyroid carcinoma using a machine learning approach

Popović-Krneta, M., Sobic-Saranovic, D. P., & Mijatović-Teodorović, L. (2023) · Medicinski podmladak · doi:10.5937/mp74-45824 · Cited by 1

IDHPre: Intradialytic Hypotension Prediction Model Based on Fully Observed Features

Yao, Y., Kuang, Z.-M., Yang, X., Wang, B., Niu, Z., Yang, J., Hu, L., Zhou, X., & Hu, P. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5689-6_9 · Cited by 1

Data-Driven Decisions: Exploring the Impact of Data Mining in Healthcare

Wang, J., & Cornely, P.-R. (2023) · International Conference on Computing and Big Data (ICCBD) · doi:10.1109/iccbd59843.2023.10607356 · Cited by 1

Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning

Song, L., Wu, X., Xu, M., Xue, L., Yu, X., Cheng, Z.-Q., Huang, C., & Miao, L. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03059-8 · Cited by 1

A Data Mining Approach to Identify Sexuality Patterns in a Brazilian University Population.

Simões, P. W. T. D. A., Cesconetto, S., de Abreu, L. L. T., de Mattos, M. C., Cassettari, J. M., Comunello, E., Ceretta, L. B., & Manenti, S. A. (2015) · PubMed · Cited by 1

Integrative and interdisciplinary challenges in translational bioinformatics

Ma, T. (2014) · ACM SIGBioinformatics Record · doi:10.1145/2661732.2661733 · Cited by 1

The Usage of Association Rule Mining to Identify Influencing Factors on Deafness After Birth

Shahraki, A. D., Safdari, R., Gahfarokhi, H. H., & Tahmasebian, S. (2015) · Acta Informatica Medica · doi:10.5455/aim.2015.23.356-359 · Cited by 1

A Novel Generic Algorithm for Robust Physiological Signal Classification

Mahdiani, S., Vanhala, J. J., & Viik, J. (2016) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-32703-7_206 · Cited by 1

Mining Outliers from Medical Datasets Using Neighbourhood Rough Set and Data Classification with Neural Network

Goh, P. Y., Tan, S. C., & Cheah, W. P. (2017) · Series in bioengineering · doi:10.1007/978-981-10-3957-7_12 · Cited by 1

Portinari: A Data Exploration Tool to Personalize Cervical Cancer Screening

Sen, S., Ribeiro, M. H., de Melo-Minardi, R. C., Meira, W., & Nygård, M. (2017) · doi:10.1109/icse-seis.2017.6 · Cited by 1

Identifying the suitable reduction technique for mining medical data

Abuzaraida, M. A., & Elramalli, A. F. (2017) · doi:10.1109/icitech.2017.8080029 · Cited by 1

Understanding Prediabetes in a Medicare Advantage Population Using Data Adaptive Techniques

Kamble, P., Collins, J. M., Harvey, R. A., PREWITT, T. G., Kimball, E. S., DeLuzio, T., Allen, E., & Bouchard, J. R. (2018) · Population Health Management · doi:10.1089/pop.2017.0165 · Cited by 1

A guideline to determine the training sample size when applying big data mining methods in clinical decision making

Daniyal, Wang, W.-J., Su, M., Lee, S.-H., Hung, C., & Chen, C. (2018) · 2018 IEEE International Conference on Applied System Invention (ICASI) · doi:10.1109/icasi.2018.8394347 · Cited by 1

Multi-Objective Approach for Support Vector Machine Parameter Optimization and Variable Selection in Cardiovascular Predictive Modeling

Brester, C., Ryzhikov, I., Tuomainen, T., Voutilainen, A., Semenkin, E. S., & Kolehmainen, M. (2018) · International Conference on Informatics in Control, Automation and Robotics (ICINCO) · doi:10.5220/0006866001990205 · Cited by 1

FRI-Feature Relevance Intervals for Interpretable and Interactive Data Exploration

Pfannschmidt, L., Göpfert, C., Neumann, U., Heider, D., & Hammer, B. (2019) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2019.8791489 · Cited by 5

A Review on Hidden Debts in Machine Learning Systems

Chaudhary, D. K., Srivastava, S. K., & Kumar, V. (2018) · International Conference on Green Computing and Internet of Things (ICGCIoT) · doi:10.1109/icgciot.2018.8753081 · Cited by 1

Screening a portfolio of pathologies by subject profiling and medical test rationing

Herazo-Padilla, N., Augusto, V., Dalmas, B., Xie, X., & Bongué, B. (2019) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2019.8843170 · Cited by 1

Understanding Antibiotic Resistance Using Different Machine Learning Approaches

Pradhan, T. P., DEBATA, N., & Swarnkar, T. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1081-6_6 · Cited by 1

Hybrid Multi Cloud based Disease Prediction Model for Type II Diabetes

Durgadevi, M., & Kalpana, R. (2020) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.c5680.029320 · Cited by 1

The Comparison of Nearest Neighbor Algorithm as Modeling in Conclusion of Interpretation of Bil Ma'tsur and Bil Ra'yi

Nur, A., Mustakim, M., Yasir, M., & Fatni, Z. (2021) · International Conference of Computer and Informatics Engineering (IC2IE) · doi:10.1109/ic2ie53219.2021.9649246 · Cited by 1

Healthcare systems

Banerjee, A., Johnson, R., Mekaroonreung, M., & Pope, B. (2013) · Industrial innovation series · doi:10.1201/b15964-18 · Cited by 1

Métodos de apoio ao diagnóstico médico por imagens usando regras de associação e redes complexas

Watanabe, C. Y. V. (2013) · doi:10.11606/t.55.2013.tde-10032014-161433 · Cited by 1

Beyond relational

Hylock, R. H. (2013) · doi:10.17077/etd.57xky8i6 · Cited by 1

Risk Prediction for Postoperative Morbidity of Endovascular Aneurysm Repair Using Ensemble Model

Hsieh, N., Chan, C.-H., & Tsai, H.-C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12179-1_43 · Cited by 1

Predicting High-cost Patients in General Population Using Data Mining Techniques

Shenas, S. A. I. (2012) · uO Research (University of Ottawa) · doi:10.20381/ruor-6153 · Cited by 1

Data Mining of the Human Being

Sanctis, M. D., & Loreti, P. (2017) · doi:10.1002/9781119341451.ch4 · Cited by 1

Modelos computacionais prognósticos de lesões traumáticas do plexo braquial em adultos

Abud, L. D. M. E. (2018) · doi:10.11606/d.45.2018.tde-20082018-140641 · Cited by 1

Novel Uncertainty Framework for Deep Learning EnsemblesPreprint

Kachman, T., Moshkovitz, M., & Rosen‐Zvi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04917 · Cited by 2

Diagnóstico de doenças mentais baseado em mineração de dados e redes complexas

Alves, C. L. (2019) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2019.tde-07032019-102825 · Cited by 1

Impact of Inferred Comorbidity Networks on Health Outcomes

Kalgotra, P. (2017) · SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University) · Cited by 1

Machine Learning Identifies the miR-196b and miR-34c-5p as the Chemotherapy Response Biomarkers of Lung AdenocarcinomaPreprint

Lan, Y., Xue, J., Chen, L., Wu, D., Wang, W., Shen, K., & Chen, P. (2020) · Research Square · doi:10.21203/rs.3.rs-54016/v1 · Cited by 1

Dynamic Re-admission Prediction of Heart Patients Using Scalable Models

Bontje, B. C. A. (2021) · Utrecht University Repository (Utrecht University) · Cited by 1

Medical Expert Systems

Kerkhof, P. L. M., Alonso‐Betanzos, A., & Moret‐Bonillo, V. (2017) · Wiley eBooks · doi:10.1002/047134608x.w1416.pub3 · Cited by 1

On the intelligent management of sepsis in the intensive care unit

Ripoll, V. J. R. (2013) · doi:10.5821/dissertation-2117-94886 · Cited by 1

Application of Random Forest Model in Cancer Risk Prognosis: A Role for Dietary HabitsPreprint

Hormozi, M., Mirmohammadkhani, M., Bagheri, M. M., Safaeian, A., Deihim, M., Parsaeian, M., & Nazari, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3906286/v1 · Cited by 1

Improving Medical Document Classification via Feature Engineering

Abdollahi, M. (2024) · doi:10.26686/wgtn.25583424 · Cited by 1

Risk Factors and Predictive Modeling for Aortic Aneurysm

Vanichbuncha, T. (2012) · KTH Publication Database DiVA (KTH Royal Institute of Technology)

Prognostication and Prediction in Anatomic Pathology: Carcinoma of the Breast as an Illustrative Model

Wick, M. R., Swanson, P. E., & Marchevsky, A. M. (2011) · Springer eBooks · doi:10.1007/978-1-4419-1030-1_5

Exploring Medical Family Tree Data Using Visual Data Mining

Zainon, W. M. N. W., & Talib, A. Z. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02958-0_39

Comparison of Classification Rates among Logistic Regression, Neural Network and Support Vector Machines in the Presence of Missing Data

Upadhyaya, S. (2014) · NDSU Repository (North Dakota State University)

Clinical Informatics to Diagnose Cardiac Diseases Based on Data Mining

Ha, S. H., & Zhang, Z. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15020-3_6

Sistematização da percepção médica na construção de sistemas para recuperação de imagens por conteúdo

da Silva, M. P. (2014) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.55.2014.tde-16052014-154931

Intelligent Clinical Information Systems for the Cardiovascular Diseases

Hsieh, N., Keh, H., Chang, C., & Chan, C.-H. (2012) · Lecture notes in electrical engineering · doi:10.1007/978-3-642-28807-4_44

Clinical Decision Support For Disease Classification Based On The Tests Association

Ha, S. H., Joo, S. H., & Kwon, E. K. (2012) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1082984

Editorial commentValidation of a prognostic model to predict survival after non-small-cell lung cancer surgery

Falcoz, P. (2010) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/j.ejcts.2010.04.004

On the Intelligent Management of Sepsis in the Intensive Care Unit

Ripoll, R., & Jordi, V. (2013) · TDX (Tesis Doctorals en Xarxa)

An Intelligent Model For Detection Of Post-Operative Infections

Rashidy, M. E., TahaTaha, Ayad, N., & HodaSroor (2011) · International Journal of Artificial Intelligence & Applications · doi:10.5121/ijaia.2011.2404

Data mining of hospital characteristics in online publication of medical quality information

Kreng, V. B., & Yang, S. (2013) · Health · doi:10.4236/health.2013.55123

Communication Intelligence: Optimising healthcare communication in a digital age

Eden, D. J., & Bloor, J. (2010) · Journal of Communications In Healthcare · doi:10.1179/175380710x12813488575808

Data Mining Using Rough Sets and Orthogonal Signal Correction-Orthogonal Partial Least Squares Analysis

Wang, Z., Nie, B., Xu, J., Hu, R., Liu, H., & Du, J. (2010) · International Forum on Information Technology and Applications · doi:10.1109/ifita.2010.197

Correction: Prediction of Peptide Reactivity with Human IVIg through a Knowledge-Based Approach

Barbarini, N., Tiengo, A., & Bellazzi, R. (2011) · PLoS ONE · doi:10.1371/annotation/57a9c527-109a-435f-a78d-1b7bce0e578b

Prognostic methods in cardiac surgery and postoperative intensive care

Verduijn, M. (2007) · TU/e Research Portal · doi:10.6100/ir632972

Applying Data Mining Technique for Predicting Incubator Length of Stay in Egypt and USA

FadyS., H., Taha El-Sayed, T., & Mervat Mahmoud, M. (2013) · International Journal of Computer Applications · doi:10.5120/12379-8726

A Decision Support System For Predicting Hospitalization Of Hemodialysis Patients

Yeh, J., & Wu, T. (2009) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1331128

Closure to “Data Mining Process for Integrated Evaporation Model” by M. E. Keskin, Ö. Terzi, and E. U. Küçüksille

Keskin, M. E., Terzi, Ö., & Küçüksille, E. U. (2010) · Journal of Irrigation and Drainage Engineering · doi:10.1061/(asce)ir.1943-4774.0000237

Reverse Engineering Measures of Clinical Care Quality: Sequential Pattern Mining

Chiu, H., & Meeker, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21843-4_17

CLINICAL DATABASE ANALYSIS USING DMDT BASED PREDICTIVE MODELLING

Indrasenan, S., & Balasubramaniam, S. (2013) · ICTACT Journal on Soft Computing · doi:10.21917/ijsc.2013.0081

Learning Relational Patterns Mined from Temporally Rich Textual Data

Tavanai, A. (2011)

Clustering as an Artificial Intelligence Technique in Drug Resistance of HIV/AIDS Patients: Case Study Botswana

Chandrasekaran, R., & Masizana-Katongo, A. (2014) · doi:10.2316/p.2014.815-005

Noninvasive prediction of HCV-4 SVR by 2D US: a randomized study using data mining algorithm

Mahfouz, H., Elrazek, A. E. A., Abdelazeem, K., EL-FATTAH, M. D. R. M. A. G. A., Foad, M., Salama, K., Elbanna, A., Bilasy, S., & Fakhry, M. (2016) · Al-Azhar Assiut Medical Journal · doi:10.4103/1687-1693.180454

The future medicine

Hussein, A. M. A. (2016) · Al-Azhar Assiut Medical Journal · doi:10.4103/1687-1693.180463

Application of Data Mining techniques to relate Cardiovascular Risk and Coronary Calcium

Lujan, F. N., Cymberknop, L. J., Alfonso, M. F., Legnani, W. E., & Armentano, R. L. (2016) · Journal of Physics Conference Series · doi:10.1088/1742-6596/705/1/012040

Guest Editorial

Tong, H., Wang, F., Choudhury, M. D., & Zoran Obradovic, A. (2016) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2912122

Naturopathic Practice at North American Academic Institutions: Description of 300,483 Visits and Comparison to Conventional Primary Care

Jolla, P. P. L. (2014)

Knowledge Discovery and Data Mining Applications in the Healthcare Industry

Barazandeh, I., & Gholamian, M. R. (2014) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-4666-6316-9.ch013

Review of Business Intelligence and Intelligent Systems in Healthcare Domain

Cebeci, H. İ., & Hızıroğlu, A. (2016) · Advances in data mining and database management book series · doi:10.4018/978-1-5225-0075-9.ch009

Rare Diseases Internet Information Retrieval and Knowledge Discovery

Blanco, J. M. L., Rodríguez, P. L. G., Rabasco, A. M., Cózar-Bernal, M. J., Suárez, M. C., González-Rodrı́guez, M. L., & Casares, M. O. (2016) · IGI Global eBooks · doi:10.4018/978-1-4666-9978-6.ch059

On the Road to Ephesus

Roff-Wexler, S. (2016) · IGI Global eBooks · doi:10.4018/978-1-4666-9840-6.ch103

Saúde em Portugal : estudo das urgências hospitalares através do Data Mining

Alves, D. D. S. (2016) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)

Success Dimensions of ICTs in Healthcare

Kaur, P. D., & Sharma, P. (2016) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-5225-0660-7.ch007

Assessment and comparison of likely density distributions in the cases of thickness measurement of skin tumours by ultrasound examination and histological analysis

Drulytė, I., Ruzgas, T., Raišutis, R., & Valiukevičienė, S. (2016) · Journal of Vibroengineering · doi:10.21595/jve.2016.17183

Design of a decision support model for the secondary prevention of diabetes

Sangi, M. (2013) · Research Online (University of Wollongong)

Investigating Predictive Disease Model Transportability through Cohort Simulation and Causal Analysis

Singleton, K. (2016) · eScholarship (California Digital Library)

Extracting Rules for Decreasing Body Fat Mass Using Various Classifiers from Daily Lifestyle Habits Data

Ushikubo, S., Kanamori, K., & Ohwada, H. (2016) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.2016070104

The Role of Data Mining in Predictive Medicine: A Narrative Review

Lin, F. P. Y. (2006)

La minería de datos en apoyo a la toma de decisiones clínicas

Zaldivar, Y. R., Pérez, J. F. R., & Pelegrin, L. S. (2022) · Revista Cubana de Transformación Digital

Predictive data mining in clinical medicine: a focus on selected methods and applications

Bellazzi, R., Ferrazzi, F., & Sacchi, L. (2011) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.23 · Cited by 106

LEVERAGING DATA MINING TECHNIQUES IN DEVELOPING AN INTEGRATED FRAMEWORK TO PREDICT THE HEALTH PREDICTIONS

Sharma, M., & Sharma, M. (2021) · International Journal of Research in Medical Sciences & Technology · doi:10.37648/ijrmst.v11i02.007 · Cited by 1

Data Mining Algorithms for Medical Sciences

Hassanzadeh, M., & Ebrahimi, S. A. R. (2013) · Medical Informatics