The Role of Artificial Intelligence in Early Detection of Chronic Diseases: Opportunities and Challenges

This study explores the application of artificial intelligence (AI), specifically machine learning models including Logistic Regression, Decision Tree, and Random Forest, in the early detection of chronic diseases, with a focus on type ۲ diabetes. Utilizing a dataset comprising clinical and lifestyle features such as age, BMI, fasting glucose, and physical activity, the models were evaluated for their predictive accuracy. The results revealed an exceptional performance, with all three models achieving ۱۰۰% accuracy on the test set, as confirmed by confusion matrices showing no misclassifications across risk classes (low, moderate, and high). Feature importance analysis, derived from the Random Forest model, identified physical activity and age as the most influential predictors, followed by systolic blood pressure, fasting glucose, and BMI, while smoking and family history had lesser impact. These findings highlight the potential of AI-driven tools to enhance early detection, offering opportunities for personalized medicine and improved patient outcomes in resource-limited settings. However, the perfect accuracy observed may indicate overfitting or insufficient variability in the small test dataset, necessitating validation with larger, more diverse datasets. Challenges such as data quality, ethical concerns regarding patient privacy, and the integration of AI into clinical workflows are critical considerations. The study underscores the importance of robust data preprocessing and model validation to ensure reliable predictions in real-world medical applications. Future research should focus on scaling these models to multicenter datasets and addressing biases to maximize clinical utility. The integration of advanced AI techniques, such as deep learning, could further refine diagnostic precision. This research contributes to the growing evidence supporting AI's transformative role in chronic disease management. It also calls for collaborative efforts among researchers, clinicians, and policymakers to establish standardized frameworks for AI deployment in healthcare. The findings suggest that while AI holds significant promise, its success depends on overcoming technical and ethical hurdles. This study lays the foundation for developing AI-based diagnostic tools tailored to chronic disease prevention. The results advocate for continued investment in AI research to address global health challenges. Ultimately, the effective use of AI in early detection could revolutionize chronic disease management, provided data integrity and model generalizability are prioritized. This abstract encapsulates the opportunities and challenges identified, paving the way for future innovations in medical AI.

کلیدواژه ها:

Artificial Intelligence ، Early Detection ، Chronic Diseases ، Machine Learning ، Healthcare Innovation