Penerapan Data Mining untuk Klasifikasi Penyakit Stroke Menggunakan Algoritma Naïve Bayes

Abstract

Stroke is a disturbance of brain function, both local and general, that occurs suddenly, progressively, and rapidly due to non-traumatic brain blood circulation disorders that lasts more than 24 hours or ends in death. Stroke is also one of the deadliest diseases in Indonesia. In this study, stroke data was used to explore new information or knowledge in it. The process of extracting new information from a set of data is known as data mining. Therefore, this research aims to classify data related to stroke using the Naïve Bayes algorithm to find out whether the patient has a stroke or not. There are 10 attributes that are included in the causes of stroke, among others, gender, age, history of hypertension, history of heart disease, marital status, type of work, type of residence, glucose level, body mass index and smoking status. The results showed that classification with the Naïve Bayes algorithm can be applied in classifying stroke data resulting in an accuracy value of 92.48% in the Good Classification category.