Published Paper Details:

The development of intelligent healthcare solutions has transformed disease diagnosis and treatment recommendation processes, addressing critical needs in personalized and accessible healthcare. This research presents an AI-driven, web-based disease prediction and recommendation system built using the Flask framework, which integrates machine learning models with symptom-based user inputs to offer personalized healthcare recommendations. Using a Support Vector Classifier (SVC) trained on symptom-disease datasets, the model predicts likely diseases based on user-inputted symptoms, then offers targeted recommendations encompassing medication, dietary suggestions, exercise routines, and preventive precautions. The system holds both technical and clinical significance, combining data-driven methods with healthcare-specific recommendations to streamline diagnosis and treatment. Technical contributions include efficient data integration from diverse sources--symptom records, medication lists, and disease information--to create a robust recommendation engine capable of supporting healthcare providers and patients alike. Clinically, the model addresses common diagnostic challenges, such as symptom ambiguity, by utilizing a probabilistic approach to enhance diagnostic accuracy. Moreover, the system prioritizes user accessibility through an intuitive web interface, making it suitable for both clinical and individual use. This AI-powered recommendation system has substantial societal implications, especially for remote or underserved populations with limited access to medical care. By providing preliminary diagnostic insights and personalized recommendations, the model can bridge gaps in healthcare access and potentially reduce the burden on healthcare facilities. In summary, this research contributes to the ongoing integration of AI in healthcare, demonstrating a scalable, user-friendly model that can enhance early diagnosis and support better health outcomes, ultimately advancing the accessibility and personalization of medical services.