The increasing breakthroughs in illness diagnosis classification and identification systems have led to a steady growth in the incorporation of machine learning in medical diagnostics. These systems provide crucial data aiding medical professionals in the early detection of fatal diseases, significantly enhancing patient survival rates. Globally, heart disease stands as the leading cause of death. The escalating rates of heart strokes among juveniles underscore the need for an early detection system to prevent potential incidents. Frequent and costly tests like electrocardiograms (ECG) are impractical for the general population. As a result, a simple and trustworthy method for estimating the risk of heart disease is suggested. This system makes use of machine learning techniques and algorithms including Support Vector Classifier (SVC), Random Forest, Naïve Bayes, and K-Nearest Neighbors (KNN). It provides a useful method of heart disease prediction by analyzing several factors that users provide through the frontend interface.I.
Identification of disease from therapeutic statistical evidences area single confronted task which can make a point of importance in the field of medical science. But according to the literature survey, it has been seen that still there are some chances that this challenging task can be fulfilled. In this research a feature ranking algorithm Random Forest is used for ranked the features of the attributes & later on four machine learning algorithm has been used i.e. Random forest, decision Tree, support Vector Machine & XG Boost classification algorithm to classify similar disease datasets like Jaundice, Malaria, Covid, Common cold, Typhoid, Dengue & Pneumonia. Comparison between the classifier is done on the basis of with ranking with feature selection & ranking without feature selection with the help of parameters of confusion matrix, Matthews’s correlation coefficient (MCC), area under the curve (AUC), Receiver Operating Characteristics Curve (ROC) & computational time. The results of the simulations shows the effectiveness of Covid like disease prediction is done by the feature selection ranking &classification algorithm.
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