Predictive Data Analytics Framework Based on Heart Healthcare System (HHS) Using Machine Learning

Abstract

Cardiovascular diseases (CVD) have recently outdid all other reasons of death universal in both developing and developed nations. Initial detection of cardiac conditions and continuing therapeutic supervision by experts can lower the death rate. However, accurate diagnosis of cardiac issues in all circumstances and 24-hour patient consultation by a doctor are still not feasible due to the increased intellect, effort, and expertise required. In this study, a basic concept for an Machine Learning (ML)-based heart disease prediction system was presented to identify impending heart disease using Machine Learning techniques. Despite the increasing number of empirical studies in this topic, particularly from underdeveloped countries, here lack many synthesised research articles in the field. In a time when the amount of data available is constantly increasing, predictive analytics has become more and more important as a tool for heart welfare services and human protection.  By utilising data collected from previous events to predict future patterns and outcomes, this state-of-the-art technology assists heart-care agencies in making more informed decisions about how to best serve their clients. However, as with any other data-driven technology, predictive analytics must be used appropriately to guarantee effective and ethical business operations. Healthcare forecasting has gained importance in recent years due to the growing popularity of AI (Artificial Intelligence) and ML (Machine Learning). In the healthcare sector, forecasting can also aid physicians in providing more precise and timely diagnoses. By anticipating likely medical events, medical staff can identify and treat individuals with greater efficiency and precision. This could lead to better patient outcomes and even cost savings.  These systems provide excellent therapeutic support and have the ability to diagnose illnesses by mimicking human cognition.  This study's included studies focus on forecasting the heart healthcare system (HHS) using machine learning algorithms. We implemented the system using the K-means Elbow technique for registration and notification, a decision tree for HHS, and MySQL for immunisation reminders.