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Journals / International Journal of 3D Printing Technologies and Digital Industry / 2026 / Cilt: 10 Sayı: 1

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

Sema Bilekyiğit(KARAMANOGLU MEHMETBEY UNIVERSITY)0000-0001-9237-3602
Ayşe Eldem(KARAMANOĞLU MEHMETBEY ÜNİVERSİTESİ)0000-0002-5561-1568
Pages
153–166
Publication Date
2026-04-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results of the experiments, the best performance was obtained from the RF algorithm with 90% accuracy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Heart failure is one of the most chronic diseases in recent years.
📊
Key Findings & Data: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.
💡
Conclusion & Impact:According to the results of the experiments, the best performance was obtained from the RF algorithm with 90% accuracy.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Heart Failure Disease · Machine Learning · Classification

Abstract

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.

Özet

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.

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Cite This Article

Bilekyiğit, S., Eldem, A. (2026). A MACHINE LEARNING BASED DECISION SUPPORT SYSTEM FOR PREDICTIVE DETECTION OF HEART FAILURE. International Journal of 3D Printing Technologies and Digital Industry, Cilt: 10 Sayı: 1, 153–166. https://doi.org/10.46519/ij3dptdi.1829744

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