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Journals / Advances in Artificial Intelligence Research / 2022 / Cilt: 2 - Sayı: 2

Machine Learning-Based Comparative Study For Heart Disease Prediction

Merve GÜLLÜ
M. Ali AKCAYOL
Necaattin BARIŞÇI
Pages
51–58
Publication Date
2022-09-23
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The experimental results show that the Random Forest algorithm is more successful than Adaboost, Bagging, Logitboost, and Support Vector machine using Cleveland and Statlog datasets.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Heart disease is one of the most common causes of death globally.
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Key Findings & Data:The proposed system consists of three components: (1) preprocess of datasets, (2) feature selection with genetic and tabu search algorithm, and (3) classification module.
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Conclusion & Impact:The experimental results show that the Random Forest algorithm is more successful than Adaboost, Bagging, Logitboost, and Support Vector machine using Cleveland and Statlog datasets.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Classification · optimization · heart disease

Abstract

Heart disease is one of the most common causes of death globally. In this study, machine learning algorithms and models widely used in the literature to predict heart disease have been extensively compared, and a hybrid feature selection based on genetic algorithm and tabu search methods have been developed. The proposed system consists of three components: (1) preprocess of datasets, (2) feature selection with genetic and tabu search algorithm, and (3) classification module. The models have been tested using different datasets, and detailed comparisons and analysis were presented. The experimental results show that the Random Forest algorithm is more successful than Adaboost, Bagging, Logitboost, and Support Vector machine using Cleveland and Statlog datasets.

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Cite This Article

GÜLLÜ, M., AKCAYOL, M. A., BARIŞÇI, N. (2022). Machine Learning-Based Comparative Study For Heart Disease Prediction. Advances in Artificial Intelligence Research, Cilt: 2 - Sayı: 2, 51–58. https://ajindex.com/articles/728994

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