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

Aug 3, 2026

Cardiovascular diseases (CVD) remain a leading cause of mortality across developing and developed nations, and early detection along with continuous expert supervision is key to reducing death rates. Yet accurate diagnosis in all scenarios and true 24-hour physician consultation are difficult due to high required intellect, effort, and expertise. The study proposes an ML-based heart disease prediction approach to identify impending cardiac conditions. With growing healthcare data and the rise of AI/ML, predictive analytics supports more timely diagnoses, better patient outcomes, and potential cost savings while emphasizing appropriate and ethical use in healthcare forecasting.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Motivation and problem statement

Role of predictive analytics and ML

Proposed system approach

Data processing and clustering (K-means)

Classification for HHS (decision tree)

Notifications and storage (MySQL)

Journal of Advanced Zoology ISSN: 0253-7214 Volume 44 Special Issue-2 Year 2023 Page 3673:3686 Predictive Data Analytics Framework Based on Heart Healthcare System (HHS) Using Machine Learning Priya Mangesh Nerkar1, Kazi Kutubuddin Sayyad Liyakat2, Bhagyarekha Ujjwalganesh Dhaware3, Kazi Sultanabanu Sayyad Liyakat4 1Sr. Research Associate, DL Solutions, Solapur. Maharashtra, India. 2Professor, Department of Electrical Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India. 3Assistant Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India. 4Research Associate, DL Solutions, Solapur. Maharashtra, India. [Email:](Email:1priyamnerkar@gmail.com)[1](Email:1priyamnerkar@gmail.com)[priyamnerkar@gmail.com](Email:1priyamnerkar@gmail.com), [2](2drkkazi@gmail.com)[drkkazi@gmail.com](2drkkazi@gmail.com), [3](3bhagyarekha.dhaware28@gmail.com)[bhagyarekha.dhaware28@gmail.com](3bhagyarekha.dhaware28@gmail.com), [4](4rajasaheb.3617@gmail.com)[rajasaheb.3617@gmail.com](4rajasaheb.3617@gmail.com) | Article History
Received: 06 June 2023
Revised: 09September 2023
Accepted:09 November 2023 | 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 outcomesand 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 | | --- | --- | 3673 Available online at: [https://jazindia.com](https://jazindia.com) Predictive Data Analytics Framework Based on Heart Healthcare System (HHS) Using Machine Learning | CC License
CC-BY-NC-SA 4.0 | system using the K-means Elbow technique for registration and notification, a decision tree for HHS, and MySQL for immunisation reminders.
Keywords: Decision Tree, K-means algorithm, Machine Learning, MySQL, Hearth healthcare system(HHS), cardiovascular diseases, | | --- | --- | 1. Introduction Heart, a muscular tissue that pumps blood around the body, makes up the majority of the circulatory system of the body, which also includes the l

FAQs

Why is early heart disease detection important in this study?

Early detection of cardiac abnormalities and prediction instruments can reduce CVD-related death rates by enabling timely intervention.

What solution does the paper propose?

An ML-based heart disease prediction system focused on forecasting the Heart Healthcare System (HHS) using algorithms including K-means and a decision tree.

Which tools or algorithms are used in the proposed framework?

The framework uses the K-means Elbow technique for registration and notification, a decision tree for HHS, and MySQL for immunisation reminders.

Aug 3, 2026

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