Artificial Intelligence in Healthcare: Diagnosis, Treatment, and Prediction

Artificial Intelligence (AI) has emerged as a transformative technology in the healthcare sector, addressing critical global challenges such as increasing patient populations, rising treatment costs, and a shortage of skilled medical professionals. By integrating machine learning, deep learning, and natural language processing, AI systems can efficiently analyze massive volumes of structured and unstructured medical data to identify patterns that often remain invisible to human analysis. This research explores the diverse applications of AI across diagnosis, treatment, and predictive analytics, highlighting how these technologies reduce human error, enhance clinical workflows, and empower patients to take an active role in managing their own health through wearable devices and virtual assistants. The technical implementation described in this study follows a client-server architecture, utilizing a Flask-based backend and a user-friendly web interface for real-time interaction between healthcare providers and AI models. Before training, medical datasets undergo rigorous preprocessing, including the removal of inconsistent records and feature scaling to ensure model reliability. A comparative analysis of machine learning models was conducted, revealing that the Random Forest algorithm outperformed the Decision Tree model across all evaluation metrics, achieving an accuracy of 95% compared to 90%. This performance gap is attributed to the Random Forest model's superior generalization capabilities and its ability to minimize overfitting, making it a more stable choice for identifying complex health conditions. By providing a powerful framework for evidence-based decision-making, AI-based healthcare systems are poised to significantly improve patient survival rates and build a more efficient, accessible healthcare infrastructure