Analisis Performa Algoritma Machine Learning pada Prediksi Penyakit Cerebrovascular Accidents

Authors

  • Robi Aziz Zuama Universitas Bina Sarana Informatika, Jakarta
  • Syaifur Rahmatullah Universitas Nusa Mandiri, Jakarta
  • Yuri Yuliani Universitas Bina Sarana Informatika, Jakarta

DOI:

https://doi.org/10.30865/mib.v6i1.3488

Keywords:

Prediction, Cerebrovascular Accidents, Stroke, Multi-Layer Perceptron, KNN, Decision Tree, Random Forest

Abstract

Cerebrovascular Accidents (stroke) are a disease that threatens and causes death and disability and disability in the world, in Indonesia the number of people affected by stroke is increasing every year. Stroke can be prevented by adopting a healthy lifestyle, eating nutritious food, and doing physical activity. The purpose of this study is to create an effective stroke prediction model, the system uses parameters from lifestyle factors, controllable factors such as medical risk factors, and uncontrollable factors. Four classification algorithms are proposed, namely multi-layer perceptron, KNN, Decision Tree, and Random Forest. The results show that the classification algorithm can work effectively by producing a perfect score of 99.99% accuracy at the 10K-Fold Validation level of validation.

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Published

2022-01-25

Issue

Vol. 6 No. 1 (2022): Januari 2022

Section

Articles

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