Open AccessExploratory Data Analysis and Machine Learning Algorithms to Classifying Stroke Disease
Author(s) -
Prismahardi Aji Riyantoko,
Tresna Maulana Fahrudin,
Kartika Maulida Hindrayani,
Mohammad Idhom
Publication year - 2021
Publication title -
international journal of computer, network security and information system (ijconsist)
Language(s) - English
Resource type - Journals
ISSN - 2686-3480
DOI - 10.33005/ijconsist.v2i02.49
Subject(s) - machine learning , computer science , artificial intelligence , exploratory analysis , logistic regression , stochastic gradient descent , exploratory data analysis , algorithm , anomaly detection , online machine learning , stroke (engine) , data mining , active learning (machine learning) , artificial neural network , data science , engineering , mechanical engineering
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.