Abstract
Fuzzy logic has emerged as a powerful tool in the medical domain, offering effective solutions for complex diagnostic tasks. It has been widely used in detecting critical conditions such as breast cancer, lung cancer, prostate cancer, and heart disease. This study presents an unsupervised classification model for early prediction of heart attacks using the Fuzzy C-Means (FCM) algorithm. The system analyzes patient medical records, utilizing 13 key attributes as input to assess heart attack risk. A dataset comprising 297 patient records was used to evaluate the model’s performance, resulting in a classification accuracy of 100%. When compared to traditional neural network models like back propagation and adaptive linear networks, the FCM-based approach demonstrated superior efficiency and cost-effectiveness. The model was developed using MATLAB’s Fuzzy Logic Toolbox and aims to support physicians in making more accurate and timely diagnoses of heart-related conditions.
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