Predictive Analysis Heart Disease Based on Machine Learning Using the Random Forest Algorithm

Anisa Handayani; Syafira Salsabila; Auliya Firdausiyah; Arif Setiawan; Yutia Nia Nesicha

doi:10.59934/jaiea.v4i3.1060

Authors

Anisa Handayani Universitas Muria Kudus
Syafira Salsabila
Auliya Firdausiyah
Arif Setiawan
Yutia Nia Nesicha

DOI:

https://doi.org/10.59934/jaiea.v4i3.1060

Keywords:

Classification, Heart Disease, Medical Dataset, Prediction, Random Forest

Abstract

Heart disease is one of the leading causes of death worldwide, requiring accurate and early detection systems. This study aims to build a predictive model for heart disease using the Random Forest algorithm based on patient medical records. The dataset used contains 1,190 patient records with 11 medical attributes. The data were preprocessed and divided into training and testing sets with an 80:20 ratio. The model was trained and evaluated using accuracy, confusion matrix, and classification report metrics. The results show that the model achieved 100% accuracy on the training data and 82.35% on the testing data. Important contributing features include max heart rate, chest pain type, old peak, and ST slope. In addition, predictions for individual patients were presented to improve interpretability. This approach demonstrates that machine learning, particularly Random Forest, can be a reliable method for early detection of heart disease and has potential for clinical decision support systems.

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