Optimizing K-Nearest Neighbor Using Ant Colony Optimization for Heart Disease Classification

Florentina Yuni Arini; Patcharanikarn Pongthanoo; Kansa Maulina Salsabila; Muhammad Raihan; Naufal Habib Muzakki

doi:10.32734/jocai.v10.i1-23647

Authors

Florentina Yuni Arini Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang
Patcharanikarn Pongthanoo Nakhon Phanom University
Kansa Maulina Salsabila Universitas Negeri Semarang
Muhammad Raihan Universitas Negeri Semarang
Naufal Habib Muzakki Universitas Negeri Semarang

DOI:

https://doi.org/10.32734/jocai.v10.i1-23647

Keywords:

Classification, K-Nearest Neighbor, SMOTE, Ant Colony Algorithm, Heart Disease

Abstract

Heart disease is one of leading causes of death globally, making early detection essential for improving clinical outcomes. This study presents a heart disease prediction approach using the K-Nearest Neighbor (KNN) algorithm, addressing class imbalance with Synthetic Minority Over-sampling Technique (SMOTE) and enhancing feature selection through Ant Colony Optimization (ACO). Exploratory data analysis identified age, gender, cholesterol, blood pressure, e xercise-Induced Angina (EIA), ST-segment depression, number of affected vessels, and thalassemia status as key indicators of disease severity. KNN model achieved 0.90 accuracy with balanced precision and recall. The employment of SMOTE improved sensitivity for the minority class, slightly reducing overall accuracy to 0.88. However, ACO as hyperparameter tuning KNN able to produce promising accuracy 0.91. This result indicate that combining KNN with metaheuristic optimization provides a reliable, interpretable method for heart disease prediction, offering valuable support for clinical decision-making and risk assessment.

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