A MACHINE LEARNING BASED DECISION SUPPORT SYSTEM FOR PREDICTIVE DETECTION OF HEART FAILURE

Öz

Heart failure is one of the most chronic diseases in recent years. In this disease, structural and functional disorders are seen in filling or pump functions of the heart. In order to eliminate the error factor of people, especially in the diagnosis of disease, models have been developed that make predictions close to the truth with Machine Learning Algorithms (MLAs). In this study, a new model with different pre-processing steps was proposed to provide the highest accuracy in the detection of heart failure disease. The preprocessing steps such as normalization, standardization, Recursive Feature Elimination (RFE) and Logistic Regression (LR) were applied to the heart failure dataset from UCI Machine Learning Repository. After that, MLAs such as K-Nearest Neighbor (KNN), Decision Trees (DT), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB) and LR were applied with 5-fold cross validation. According to the results of the experiments, the best performance was obtained from the RF algorithm with 90% accuracy. The predictions of the proposed machine learning-based decision support system, which is interpreted with explainable artificial intelligence, will help doctors diagnose heart patients more effectively.

Anahtar Kelimeler

• Heart Failure Disease
• Machine Learning
• Classification
• Computer Aided Diagnosis System
• Medical Decision Systems.

Kaynakça

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