Exploratory Data Analysis and Machine Learning Algorithms to Classifying Stroke Disease

Riyantoko, Prismahardi Aji
Fahrudin, Tresna Maulana
Hindrayani, Kartika Maulida
Idhom, Mohammad

International Journal Of Computer, Network Security and Information System (IJCONSIST)
Vol 2 No 02 (2021): March

This paper presents data stroke disease that combine exploratory data analysis and machine learning algorithms. Using exploratory data analysis we can found the patterns, anomaly, give assumptions using statistical and graphical method. Otherwise, machine learning algorithm can classify the dataset using model, and we can compare many model. EDA have showed the result if the age of patient was attacked stroke disease between 25 into 62 years old. Machine learning algorithm have showed the highest are Logistic Regression and Stochastic Gradient Descent around 94,61%. Overall, the model of machine learning can provide the best performed and accuracy.