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Artificial intelligence-based framework for early detection of heart disease using enhanced multilayer perceptron
1Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia.
Insights
Machine learning offers a reliable alternative for detecting cardiac disease. An Enhanced Multilayer Perceptron (EMLP) model achieved 92% accuracy, outperforming traditional methods for early heart disease prediction.
Area of Science:
- Cardiology
- Machine Learning
- Artificial Intelligence in Healthcare
Background:
- Cardiac disease is a leading cause of global mortality, with manual detection methods facing limitations.
- Accurate and timely diagnosis of heart conditions is crucial for preventing heart failure and improving patient outcomes.
- Machine learning algorithms present a promising approach to overcome the subjectivity and variability of traditional cardiac disease detection.
Purpose of the Study:
- To introduce a novel Enhanced Multilayer Perceptron (EMLP) framework for improved cardiac disease detection.
- To enhance predictive accuracy in classifying patients with heart conditions.
- To evaluate the efficacy of the proposed EMLP model against existing methods.
Main Methods:
- Development of a novel Enhanced Multilayer Perceptron (EMLP) framework.
- Implementation of data refinement techniques to optimize the classification model.
- Assessment of the EMLP model using the CDC cardiac disease dataset.
Main Results:
- The Enhanced Multilayer Perceptron (EMLP) model achieved a classification accuracy of 92%.
- The proposed framework demonstrated superior performance compared to traditional cardiac disease detection methods.
- EMLP showed higher accuracy, precision, F1-score, and recall in experimental results.
Conclusions:
- The Enhanced Multilayer Perceptron (EMLP) framework shows significant potential for the early detection and prediction of cardiac-related diseases.
- The study underscores the efficacy of the EMLP model in accurately identifying cardiac conditions.
- This machine learning approach offers a dependable alternative for enhancing patient survival rates through prompt diagnosis.
Abstract:
Cardiac disease refers to diseases that affect the heart such as coronary artery diseases, arrhythmia and heart defects and is amongst the most difficult health conditions known to humanity. According to the WHO, heart disease is the foremost cause of mortality worldwide, causing an estimated 17.8 million deaths every year it consumes a significant amount of time as well as effort to figure out what is causing this, especially for medical specialists and doctors. Manual methods for detecting cardiac disease are biased and subject to medical specialist variance. In this aspect, machine learning algorithms have proved to be effective and dependable alternatives for detecting and classifying patients who are affected by heart disease. Precise and prompt detection of human heart disease can assist in avoiding heart failure within the initial stages and enhance patient survival. This study proposed a novel Enhanced Multilayer Perceptron (EMLP) framework complemented by data refinement techniques to enhance predictive accuracy. The classification model asses using the CDC cardiac disease dataset and achieved 92% accuracy by surpassing all the traditional methods. The proposed framework demonstrates significant potential for the early detection and prediction of cardiac-related diseases. Experimental results indicate that the Enhanced Multilayer Perceptron (EMLP) model outperformed the other algorithms in terms of accuracy, precision, F1-score, and recall, underscoring its efficacy in cardiac disease detection.
