Artificial Intelligence-Driven Predictive Models for Identifying Risk Factors of Chronic Diseases

Diabetes mellitus is a chronic metabolic disease that is a significant global public health concern. Complications may potentially be avoided or postponed with early diabetes diagnosis and treatment. The development of ML and DL has created new possibilities in the analysis of clinical data, allowing to identify the pattern concealed in them and increase the accuracy of diagnosis. This research proposes a diabetes prediction model based on the PIMA Indian Diabetes Dataset through the application of the Machine Learning (ML) and Deep Learning (DL) methods. Random Forest (RF) and Long Short-term Memory (LSTM) are two high-performance models that were implemented to extract nonlinear and temporal relationships in the data. Experimental testing showed that RF had 97.54% accuracy and 96.32 F1-score, whereas LSTM had a steady 98.85% accuracy and 98.20 F1-score. The proposed RF and LSTM models showed a clear superiority when compared to more traditional models, like Naive Bayes, Decision Trees, AdaBoost, and SVM (72-79% accuracy), to the more advanced models, like ANN, CNN-LSTM, and DNN (89-93% accuracy). The proposed framework demonstrates that combining ensemble and sequential learning offers a scalable and accurate solution for early diabetes detection, representing a key contribution to clinical decision support.