ANN-Powered Lifestyle Risk Prediction in Tech Industry Professionals

Stress and obesity are the most prevalent lifestyle-related health issues that significantly negatively impact a person's physical and mental health and often lead to chronic illnesses such as diabetes, anxiety disorders, and cardiovascular diseases. Obesity is characterized by high accumulation of body fat, which in turn increases the chances of metabolic syndromes, whereas stress, especially chronic stress, has been associated with physiological effects such as inflammation and immunological dysfunction, in addition to mental health problems. This paper investigates the possibility of using deep learning to predict the level of stress and obesity of an individual in a high-stress workplace setting, especially among IT workers. We applied Artificial Neural Network (ANN) to classify health risk levels based on two datasets emphasizing lifestyle characteristics associated with stress and obesity. We have used the SoftMax layer for multi-class classification and dense layers with ReLU activation for feature extraction. Our ANN models include the stress model and the obesity model with high prediction accuracy. Our deep learning approach provides a proactive answer for the management of such lifestyle diseases by accurately forecasting the risk levels and developing customized recommendations for lifestyle interventions. Thus, therefore indicates a potential for the assimilation of predictive analytics within the workplace wellness initiatives.