Optimized Random Forest Classifier Based on Genetic Algorithm for Heart Failure Prediction

Siregar, Maria Ulfah and Setiawan, Ichsan and Akmal, Najmunda Zia and Wardani, Dewi and Yunitasari, Yessi and Wijayanto, Ardhi (2023) Optimized Random Forest Classifier Based on Genetic Algorithm for Heart Failure Prediction. In: 2022 Seventh International Conference on Informatics and Computing (ICIC), 8-9 Desember 2022, Bali dan Online.

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Heart failure is a serious long-term condition that usually gets worse over time. On the other hand, some people do not aware to check their heart health regularly. In this study, the Random Forest will be optimized using the Genetic Algorithm to obtain the best parameters and will be applied to the heart failure dataset from Kaggle. We experimented with two iterations for every nine combinations of the parameters. We compared the results of optimized random forest, standalone random forest, decision tree, and Na¨ıve Bayes algorithms. Our finding is that the optimized method is slightly better than the other algorithms. The best F1-score is obtained at the second iteration which is 0.90789 compared to 0.89404 obtained with the sole random forest, 0.85034 obtained with the decision tree, and 0.86195 obtained with Naive Bayes. The best recall value is 0.91925 obtained in the first iteration, and in the second iteration. The best recall is also obtained with the sole random forest algorithm. The best precision value is 0.89937 which was obtained in the first. By these results, the optimized random forest algorithm could be used to result in reliable predictions about heart failure.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: decision tree; Naive Bayes; F1-score; recall; precision
Subjects: Tehnik Informatika
Divisions: Fakultas Sains dan Teknologi > Informatika (S2)
Depositing User: Maria Ulfah Siregar S.Kom. MIT., Ph.D.
Date Deposited: 31 Mar 2023 12:52
Last Modified: 31 Mar 2023 12:52
URI: http://digilib.uin-suka.ac.id/id/eprint/57587

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