Analisis Performa Algoritma Machine Learning pada Prediksi Penyakit Cerebrovascular Accidents

Robi Aziz Zuama, Syaifur Rahmatullah, Yuri Yuliani

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.

Keywords


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

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

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