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Machine learning algorithms for heart disease diagnosis: A systematic review
Yian Mao1, Bahiru Legesse Jimma2, Tefera Belsty Mihretie3
1Bioscience and Biomedical Engineering Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, Guangdong 511400, China.
Insights
Supervised machine learning aids heart disease diagnosis. Decision Trees are the most effective and widely used algorithm, outperforming others like Logistic Regression and Naive Bayes for predicting cardiac conditions.
Area of Science:
- Cardiology
- Machine Learning
- Data Science
Background:
- The heart's function is critical for overall health, with malfunctions impacting multiple bodily systems.
- Machine learning (ML) offers advanced capabilities in cardiology for predicting and diagnosing heart diseases.
- ML algorithms analyze clinical data to identify patterns for early detection and personalized treatment.
Purpose of the Study:
- To evaluate the most widely used and accurate supervised machine learning algorithms for heart disease prediction and diagnosis.
Main Methods:
- A systematic analysis of research articles from 2013-2024 was performed.
- Six major academic databases (Scopus, PubMed, ScienceDirect, Dimensions, ProQuest, IEEE) were utilized.
- The review focused on supervised machine learning algorithms applied to heart disease diagnosis.
Main Results:
- Twenty-four studies on supervised ML for heart disease were identified.
- Decision Trees, Logistic Regression, Naive Bayes, Random Forests, and Artificial Neural Networks were prominent algorithms.
- Decision Trees demonstrated the best performance and highest application frequency, followed by Logistic Regression and Naive Bayes.
Conclusions:
- Supervised machine learning shows significant promise in advancing heart disease diagnosis.
- Identifying optimal algorithms can improve prediction accuracy and guide future research.
- This analysis provides insights into effective ML approaches for cardiovascular health.
Background:
The heart is a vital organ that pumps blood throughout the body. Its proper functioning is crucial for maintaining overall health, and any malfunction can significantly impact other bodily systems. Recently, machine learning has emerged as a valuable tool in cardiology, enhancing the prediction and diagnosis of heart diseases. By analyzing clinical data, these algorithms reveal patterns that traditional methods might miss, aiding in early detection and personalized treatment. This study aimed to evaluate the most widely used and accurate supervised machine-learning algorithms for predicting and diagnosing heart disease.
Methods:
A systematic analysis was conducted using research articles obtained from six reputable academic databases: Scopus, PubMed, ScienceDirect, Dimensions, ProQuest, and IEEE. The review covers the years from 2013 to 2024. The focus was on the application of various supervised machine-learning algorithms for diagnosing heart disease.
Result:
The study identified twenty-four relevant studies that examined the use of supervised machine learning algorithms for diagnosing and predicting heart disease. Among these, five algorithms were prominent: Decision Trees, Logistic Regression, Naive Bayes, Random Forests, and Artificial Neural Networks. Decision Trees were found to be the most commonly applied and best-performing algorithm, followed by Logistic Regression and Naive Bayes. However, Artificial Neural Networks and Random Forests received less attention despite their potential for high accuracy in certain contexts.
Conclusion:
The research findings highlight important trends in heart disease prediction models using supervised machine learning. By examining these trends, researchers can identify algorithms that improve forecasting accuracy, guiding future research objectives and advancing the effectiveness of heart disease diagnosis.
