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12 November 2024 Heart failure prediction: a comparative analysis of machine learning algorithms
Fida Husain Dahri, Asif Ali Laghari, Dileep Kumar Sajnani, Asima Shazia, Teerath Kumar
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133952J (2024) https://doi.org/10.1117/12.3049024
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
In recent, machine learning techniques have been employed to predict various diseases such as lung, blood, liver, heart, etc. As the heart is considered one of the human body’s significant organs, this research proposes a comparative analysis of heart disease failure using various machine learning algorithms. Since the ECG and EEG are primary sources for analyzing heart performance, the lipid profile also provides information to determine good and bad cholesterol levels. Although this research proposes a comparative analysis of Heart Failure (HF) and other Cardiovascular diseases (CVDs), it assists in finding a better way to predict the root cause of HF and CVDs. The machine learning models are employed and trained, such as the Support Vector Classifier (SVC), Random Forest Classifier (RF), Logistic Regression (LR), Decision Tree Classifier (DT), and K-Nearest Neighbors Classifier (KNN) on the real-time dataset from Kaggle to predict and classify heart disease patients. The experimental set-up outcomes show that the Logistic Regression (LR) classifier has proven the best accuracy (88.00%) among all other machine learning classifiers. Our research results significantly contribute to predicting (HF) and nurturing advancements in AI-powered tools for improved heart failure (HF) prediction and patient care.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Fida Husain Dahri, Asif Ali Laghari, Dileep Kumar Sajnani, Asima Shazia, and Teerath Kumar "Heart failure prediction: a comparative analysis of machine learning algorithms", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133952J (12 November 2024); https://doi.org/10.1117/12.3049024
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KEYWORDS
Heart
Cardiovascular disorders
Machine learning
Chemical vapor deposition
Data modeling
Lawrencium
Cross validation
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