Melady Classification Enhancing Accuracy with Supervised Learning Methods

About one person dies every minute from heart disease, consequently, it has surpassed war as the largest cause of death in the twenty-first century. This includes both the male and female demographics, as well as the ratio may fluctuate depending on age bracket & sex. Nothing here suggests that persons of various ages are immune to cardiovascular disease. Classifying the underlying causes and diseases associated with this condition has become more difficult in recent years. In this study, we explore the different classification methods and the Flask Framework used for cardiac illness. As a result, it is drawing attention as a "fatal disease" that may kill a person in the absence of any outward signs of illness. Particularly important in cardiology, early and accurate diagnosis of heart illness is a cornerstone of effective healthcare. Classifying cardiac illness might be difficult at times because of a general lack of funds in the healthcare profession. The medical community and their patients stand to gain a great deal by making use of appropriate technological help in this area. This problem may be handled by using Data Science methodologies. There are now established tendencies in Data Science, and a wide range of research is being conducted in this area. This research seeks to apply the RF, KNN, Naive Bayes algorithm techniques to create a classifier model for the of Cardiovascular disease. We opted for the Flask framework in bringing to life our ingenious plan. This makes it easier to label needed values straight on the page thanks to HTML and CSS, marrying our pre-created models accurately to selected variables. What's great is the website's inviting user-friendly structure. Its well-structured layout simplifies deciphering categorized values. Plus, one major charm of our optimized site is that it helps users promptly get an insight into the status of an illness even before it's fully blown.