APPLICATION OF INFORMATION GAIN BASED WEIGHTED LVQ FOR HEART DISEASE DIAGNOSIS
DOI:
https://doi.org/10.53273/585pfb67Abstract
Heart disease remains one of the leading causes of mortality worldwide, making early and accurate diagnosis critically important. Machine learning techniques have increasingly been applied to support clinical decision-making. This study proposes an enhanced Learning Vector Quantization (LVQ) model incorporating Information Gain-based feature weighting to improve classification performance in heart disease diagnosis. By assigning weights to features based on their relevance, the proposed method reduces the impact of irrelevant attributes and enhances predictive accuracy. Experimental results demonstrate that the Information Gain Weighted LVQ model outperforms traditional LVQ and several benchmark classifiers in terms of accuracy, precision, recall, and F1-score.
Keywords: Heart Disease Diagnosis, Learning Vector Quantization, Information Gain, Feature Weighting, Machine Learning
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