Abstract
Decision in medical diagnosis is mostly taken by expert's experiences. In many cases, not all the expert's experiences contribute towards effective diagnosis of a disease. Researchers have taken multiple approaches like attribute reduction, rule extraction, fuzzy model optimization, etc. But noisy data in datasets, irrelevant attributes, and lack of effective fuzzy rules are major hindrances to provide best decision. In this study, we propose genetic algorithm based fuzzy decision support system for predicting the risk level of heart disease. Our proposed fuzzy decision support system (FDSS) works as follows: i) Preprocess the dataset, ii) Effective attributes are selected based on different methods, iii) Weighted fuzzy rules are generated on the basis of selected attributes using GA, iv) Build the FDSS from the generated fuzzy knowledge base, v) Predict the heart disease. The experiments carried out with real-life data set show the effectiveness of this proposed innovative approach.