Comprehensive Analysis of Multiple Diseases using Machine Learning

In the contemporary landscape of the healthcare industry, the pervasive integration of computer-based technology has resulted in the prolific accumulation of electronic health data. However, this abundance of information poses a significant challenge for medical professionals, impeding their ability to accurately analyze symptoms and promptly detect diseases. Addressing this issue, supervised Machine Learning (ML) algorithms have emerged as a promising solution, showcasing superior performance compared to traditional diagnostic methods. This literature review aims to discern patterns in disease diagnosis across various supervised ML models, with a specific focus on predicting fungal infections, diabetes, jaundice, malaria, and heart diseases. The model faces challenges in managing complex disease data, addressing class imbalances, and optimizing feature selection. Disease datasets present complexities in symptom-disease relationships, while imbalances in disease occurrences impact model accuracy. Among the myriad of algorithms explored, particular attention is directed towards the efficacy of Random Forest and Decision Trees. The proposed model aims to develop robust techniques for handling complex disease data, addressing class imbalances, and optimizing feature selection. These objectives target the improvement of disease prediction models, enhancing their accuracy and reliability for practical applications. Through an in-depth analysis of performance indicators, this study intends to contribute valuable insights into enhancing the diagnostic capabilities of healthcare professionals, facilitating early identification of high-risk conditions, and ultimately improving patient outcomes.