Artificial Neural Networks in Predicting and Managing Lifestyle-Associated Diseases: A Comprehensive Review

Lifestyle-associated diseases (LADs), including diabetes mellitus, hypertension, obesity, metabolic syndrome, and cardiovascular disorders, have emerged as the most significant contributors to global morbidity and mortality in the 21st century. Rapid urbanization, sedentary lifestyles, unhealthy dietary patterns, stress, and environmental changes have accelerated the prevalence of these chronic non-communicable diseases (NCDs). Traditional epidemiological and statistical models, while useful, often struggle to capture the complex, nonlinear, and multidimensional relationships among genetic, behavioral, environmental, and socio-economic determinants of lifestyle diseases. Artificial Neural Networks (ANNs), a subset of artificial intelligence inspired by biological neural systems, have demonstrated exceptional capability in modeling nonlinear interactions, handling high-dimensional datasets, and learning from heterogeneous clinical and behavioral data. This comprehensive review critically examines the application of ANN models in predicting, diagnosing, monitoring, and managing lifestyle-associated diseases. It synthesizes findings from peer-reviewed studies across medical informatics, computational biology, and clinical research domains, evaluating the performance of ANN-based systems in comparison with traditional statistical approaches. The review highlights that ANN models consistently outperform conventional regression-based models in predictive accuracy, sensitivity, and specificity, particularly in early disease detection and risk stratification. Furthermore, ANNs have shown promise in personalized treatment planning, continuous patient monitoring using wearable sensor data, and integration within clinical decision support systems (CDSS). However, despite their strong predictive performance, challenges such as model interpretability (black-box problem), data privacy concerns, lack of standardized datasets, and clinical validation barriers limit widespread implementation. The paper concludes that ANN-based healthcare solutions represent a transformative approach in combating lifestyle diseases by shifting the focus from reactive treatment to proactive prediction and personalized management. Future research directions emphasize explainable AI frameworks, federated learning models, multi-center validation trials, and integration with wearable and IoT technologies for real-time health analytics.