DECISION TREE OPTIMIZATION IN HEART FAILURE DIAGNOSTICS: A PARTICLE SWARM OPTIMIZATION APPROACH

Sumarna Sumarna; Sartini Sartini; Witriana Endah Pangesti; Rachmat Suryadithia; Verry Riyanto

doi:10.52436/1.jutif.2024.5.3.1815

Sumarna Informatics, Faculty of Information Technology, Universitas Nusa Mandiri, Indonesia
Sartini Informatics, Faculty of Information Technology, Universitas Nusa Mandiri, Indonesia
Witriana Endah Pangesti Information Systems, Faculty of Information Technology, Universitas Nusa Mandiri, Indonesia
Rachmat Suryadithia Information Systems, Faculty of Informatics Engineering, Universitas Bina Sarana Informatika, Indonesia
Verry Riyanto Information Systems, Faculty of Informatics Engineering, Universitas Bina Sarana Informatika, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.3.1815

Keywords: Data Mining, Decission Tree, Heart Failure Diagnosis, Optimization, Particle Swarm Optimization

Abstract

The rapid advancement of technology has made the implementation of accurate diagnostic methods for serious diseases like heart failure extremely important. Heart failure, being a leading cause of death worldwide, necessitates precise and accurate diagnostic techniques. The problem with conventional diagnostic methods is that they often fail to effectively accommodate the complexity of clinical data, leading to an increase in mortality rates due to heart failure. Previous research has employed various data analysis methods, but there are still fluctuations in the accuracy of results. The aim of this study is to enhance the accuracy of heart failure diagnosis by integrating the Decision Tree (DT) method with Particle Swarm Optimization (PSO) optimization. This research involves collecting and preprocessing heart failure data, followed by the development of a DT model. This model is then optimized using the PSO technique. The study uses a dataset from the UCI Repository, involving testing and validation processes to measure the model's effectiveness. The results show a significant improvement in accuracy and the Area Under Curve (AUC) after applying PSO. Accuracy increased from 79.92% to 85.29%, and AUC from 0.706% to 0.794%. The conclusion is that the integration of DT and PSO successfully improved the accuracy and reliability of the model in diagnosing heart failure. This innovation offers potential for further research in integrating optimization techniques in health data analysis, with the possibility of application in various clinical scenarios.

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