Multiple Disease Detection System

In the realm of healthcare, the early detection of multiple diseases presents a formidable challenge but holds immense potential for improving patient outcomes. This paper proposes an integrative approach for the simultaneous detection of four prevalent diseases: heart disease, Parkinson's disease, diabetes, and skin cancer. Leveraging advanced machine learning techniques, our framework encompasses data preprocessing methods for cleaning and normalization, feature selection strategies to extract discriminative features from heterogeneous medical datasets, and ensemble classification models for accurate disease prediction. Real-world datasets encompassing diverse medical conditions are utilized to evaluate the efficacy of the proposed framework. Experimental results demonstrate its superior performance in terms of accuracy, sensitivity, and specificity compared to conventional methods. This framework stands as a testament to the transformative potential of technology in healthcare, facilitating early detection and intervention across multiple diseases, thereby enhancing patient care and quality of life.