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SVM Decision Support for Heart Disease by manhentai20 is a document available to read on EtoBox.
This report details a decision support system for heart disease classification utilizing a support vector machine (SVM) and an integer-coded genetic algorithm for feature selection. The system achieved an overall accuracy of 72.55% in a five-class classification and 90.57% in a binary classification of disease presence, indicating its potential as a practical diagnostic tool. The methodology leverages the Cleveland heart disease database and employs a fast iterative algorithm to optimize classification perf
- Author
- manhentai20
- Language
- EN