Sistem Pakar Prediksi Obesitas Menggunakan Metode Forward Chaining
DOI:
https://doi.org/10.47065/jieee.v5i4.2839Keywords:
Obesity Prediction; Expert System; Forward Chaining; Rule-Based Reasoning; Lifestyle and Dietary FactorsAbstract
Obesity is a growing health problem that increases the risk of various chronic diseases such as diabetes, hypertension, and heart disease. Obesity status is typically determined using the Body Mass Index (BMI); however, this approach is considered inadequate for comprehensively assessing a person’s condition because it does not account for lifestyle factors and daily behaviors. This study aims to develop a rule-based expert system using Python and the Forward Chaining method to predict 7 body weight categories. The system employs a hybrid approach through balanced weighting, specifically 50% quantitative medical assessment (BMI) and 50% qualitative behavioral assessment (rule scores) based on 11 relevant attributes from the UCI dataset, such as dietary patterns, physical activity, and genetics. Performance testing results using a Confusion Matrix on 150 test data points demonstrate the system’s very high reliability, with an accuracy of 98.00% (147 correct predictions). There were 3 prediction errors, specifically between Overweight Level 2 and Level 1, due to the influence of active physical activity. This deterministic expert system has proven to be effective, stable, and applicable as a tool for education and self-directed early detection of obesity risk for the public.
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