An Intelligent Approach to Health Risk Analysis: Integration of Fuzzy C-Means and Rule-Based Systems
DOI:
https://doi.org/10.56861/inheriten.v1i1.11Keywords:
Fuzzy C-Means, Health Risk Profiling, Lifestyle Analysis, Non-Communicable Diseases, Fuzzy Rule-Based System, Preventive Healthcare, Data Clustering, Hybrid Intelligence SystemAbstract
This study addresses the growing prevalence of non-communicable diseases (NCDs) such as hypertension, diabetes, and obesity, which are significantly influenced by unhealthy lifestyle patterns including sedentary behavior, high consumption of processed foods, and elevated stress levels. As these conditions increasingly affect individuals in their productive age, particularly in developing countries like Indonesia, data-driven health risk mapping becomes crucial. The research applies the Fuzzy C-Means (FCM) clustering algorithm to classify individuals based on lifestyle-related risk profiles, handling inherent ambiguity and uncertainty in health data. To enhance interpretability and generate actionable insights, a Fuzzy Rule-Based System (FRBS) is developed to analyze the dominant contributing factors and recommend personalized interventions. The integration of FCM and FRBS provides a hybrid approach that supports public health monitoring and decision-making. Results confirm the effectiveness of this methodology in identifying health risk clusters and offering targeted recommendations, indicating its potential as a strategic tool in preventative healthcare.
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