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Journals / Turkish Journal of Forecasting / 2024 / Volume: 8 Issue: 2

Heart Attack Analysis and Prediction with Machine Learning Techniques

Shuaib Jasim(Kto karatay üniversitesi)0009-0000-5574-7302
İbrahim Onaran0000-0002-7769-4077
Mustafa Al-asadi0000-0002-8218-3458
Pages
—
Publication Date
2024-09-13
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AI Summary & Key Takeaways

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

The study highlights the potential of machine learning in assisting healthcare professionals and individuals in determining heart attack risk and taking preventive measures.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study explores the use of machine learning algorithms to analyze and predict heart attacks, focusing on genetics, lifestyle, medical history, and biometric factors.
📊
Key Findings & Data:Support vector machines were found to be the most effective model for predicting heart attack risk, with a high accuracy rate and low error rate.
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Conclusion & Impact:The study highlights the potential of machine learning in assisting healthcare professionals and individuals in determining heart attack risk and taking preventive measures.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:التعلم الالي · النوبة القلبية · البايثون

Abstract

This study explores the use of machine learning algorithms to analyze and predict heart attacks, focusing on genetics, lifestyle, medical history, and biometric factors. The data was analyzed using logistic regression, support vector machines, decision trees, and random forests. Support vector machines were found to be the most effective model for predicting heart attack risk, with a high accuracy rate and low error rate. The study highlights the potential of machine learning in assisting healthcare professionals and individuals in determining heart attack risk and taking preventive measures.

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Cite This Article

Jasim, S., Onaran, İ., Al-asadi, M. (2024). Heart Attack Analysis and Prediction with Machine Learning Techniques. Turkish Journal of Forecasting, Volume: 8 Issue: 2. https://doi.org/10.34110/forecasting.1489839

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