Penerapan Data Mining Dan Tekonologi Machine Learning Pada Klasifikasi Penyakit Jantung

Abstract

Abstrak Saat ini, dalam dunia kesehatan, data analisis dapat diproses untuk mendeteksi dan mendiagnosa penyakit. Dengan perkembangan teknologi, peranan data mining, dan kebutuhan studi digunakan untuk memecahkan masalah tersebut. Abstract In today's healthcare, data analysis can be processed to identify and diagnose various kind of diseases. Through technological development, the role of data mining and study purposes are used to solve the problem. In this case, we decided to classify heart disease using 3 kind of machine learning techniques: Logistic Regression, K-Nearest Neighbors, Random Forest, and Tuned K-Nearest Neighbors with the python programming language. The dataset in this research contained 13 features, 1 label, and 303 instances where 139 of them had cardiovascular and the other 164 were healthy. Measurement by Accuracy, Precision, Recall, and F-measure applied in order to compare all performance of each techniques. The results shown that Logistic Regression performed as the best technique with highest accuracy by 88,52%.