Machine Learning-Enabled Healthcare Decision Support System: An Advanced Disease Prediction with Patient-Doctor Collaboration

In this research, a revolutionary healthcare software system that gathers various patient symptoms in order to anticipate and assess diseases is presented. To generate predictions, the system uses five different algorithms: Random Forest, XG Boost, Support Vector Machine, Logistic Regression, and Naive Bayes. The most accurate prediction is chosen as the final result after a comparative study of the forecasts and accuracies from each method. Healthcare practitioners can make better decisions when using a graphical representation. A noteworthy development in proactive healthcare is demonstrated by the system's introduction of a function that recommends appropriate doctors to the patient. This invention supports both individual well-being and more general public health goals.