Diabetes, a significant global health crisis, is primarily driven in India by unhealthy diets and sedentary lifestyles, with rapid urbanization amplifying these effects through convenience-oriented living and limited physical activity opportunities, underscoring the need for advanced preventative strategies and technology for effective management. This study integrates Shapley Additive explanations (SHAPs) into ensemble machine learning models to improve the accuracy and efficiency of diabetes predictions. By identifying the most influential features using SHAP, this study examined their role in maintaining high predictive performance while minimizing computational demands. The impact of feature selection on model accuracy was assessed across ten models using three feature sets: all features, the top three influential features, and all except these top three. Models focusing on the top three features achieved superior performance, with the ensemble model attaining a better performance in most of the metrics, outperforming comparable approaches. Notably, excluding these features led to a significant decline in performance, reinforcing their critical influence. These findings validate the effectiveness of targeted feature selection for efficient and robust clinical applications.

In Industry 5.0, predicting electric vehicle energy usage enhances efficiency and sustainability, optimizes charging infrastructure, and meets consumer demands. Leveraging IoT, AI, and analytics enables smart charging that aligns with renewable energy goals, addresses infrastructure limitations, and promotes sustainable transportation by improving user experience, cutting costs, and boosting trust in electric vehicles. This research aims to create a synthetic data set for electric vehicles using an enhanced Generative adversarial network model and, from that, predict the energy for charging electric vehicles using ensemble Machine Learning algorithms. The importance of more detailed features for the best-performing machine learning model has been done utilizing Explainable Artificial Intelligence, specifically, the Shapley Additive Explanations approach, to provide more understanding and derive the inter-dependency among features. The enhanced TemporalCharge Generative adversarial network model provides Skewness and Kurtosis values as -0.51 and -0.182, respectively, for synthetically generated data for the city of Berhampur, Odisha, India, which is very close to four-wheeler electric vehicle charging data of the city. Several plots illustrate the influence of key features on electric vehicle energy consumption during charging, which enhances user optimization, owner empowerment, and ecosystem sustainability.

Multi-disease conditions strain the body’s defenses, complicating recovery and increasing mortality risk. Therefore, effective concurrent prevention of multiple diseases is essential for mitigating complications and improving overall well-being. Explainable artificial intelligence (XAI) with an advanced multimodal large language model (LLM) can create an interactive system enabling the general public to engage in natural language without any specialized knowledge prerequisites. Counterfactual explanation, an XAI method, offers valuable insights by suggesting adjustments to patient features to minimize disease risks. However, addressing multiple diseases simultaneously poses challenging barriers. This article proposes an interactive multi-disease prevention system that uses Google Gemini Pro, a multimodal LLM, and a non-dominated sorting genetic algorithm, namely NSGA-II, to overcome such problems. This system recommends changes in feature values to concurrently minimize the risk of diseases such as heart attacks and diabetes. The system facilitates personalized feature value selection, significantly reducing disease attack probabilities to as low as possible. Such an approach holds the potential to simultaneously address the unresolved issue of preventing and managing multiple diseases for the general public.