Enhancing Heart Disease Prediction Through Cluster-Visualized Distributed Machine Learning Paradigms for Secure Healthcare

Summary

This study develops a scalable framework for heart disease prediction using Hadoop MapReduce and K-Means clustering. Adjusting classification cut-offs affects detection sensitivity. CViHDKNN improves true positive detection while controlling false positives, whereas CViHDDT reduces false positives but may miss some heart disease cases.

Abstract

Heart disease remains one of the leading causes of mortality worldwide, creating an urgent need for accurate and scalable predictive systems that enable early diagnosis and timely clinical intervention. Traditional machine learning approaches often struggle to efficiently process large-scale medical datasets and lack interpretability, limiting their usefulness for supporting clinical decision-making. To address these challenges, this study proposes a Cluster Visualized Distributed Machine Learning framework for heart disease prediction. The framework incorporates two distributed algorithms: Cluster Visualized Hadoop Distributed Decision Tree (CViHDDT) and Cluster Visualized Hadoop Distributed K-Nearest Neighbor (CViHDKNN). The proposed models leverage Hadoop’s MapReduce framework for distributed computation across large datasets, while integrating K-Means clustering for improved data organization and visualization. This cluster-based visualization enhances interpretability by allowing clinicians to better understand relationships among patient risk factors and prediction outcomes. Experimental evaluation was conducted using the UCI Heart Disease dataset in a Hadoop-based distributed environment. The results show that CViHDKNN achieved superior predictive performance, achieving 85.25% accuracy and 88% recall, outperforming the CViHDDT model, which achieved 80.33% accuracy. Adjusting classification cut-off values also influenced sensitivity and detection rates: lower cut-offs improved true-positive detection while maintaining acceptable false-positive levels. These findings demonstrate that clustering-enhanced distributed learning improves scalability, predictive accuracy, and clinical interpretability for heart disease prediction.

Introduction

Heart disease is one of the leading causes of death worldwide and represents a major public health challenge. The rising prevalence of cardiovascular diseases highlights the urgent need for advanced diagnostic models that support early detection and treatment. Traditional diagnostic approaches rely heavily on manual evaluation and clinical judgment, which often struggle to manage large volumes of medical data efficiently. Recently, artificial intelligence (AI) and machine learning (ML) techniques have played an important role in predictive healthcare analytics, enabling data-driven disease prediction and improving risk assessment accuracy in medical systems

Protocol

Dataset acquisition

The UCI Heart Disease dataset is a widely used dataset in medical and machine learning research for predicting heart disease. It contains various clinical and diagnostic characteristics of patients, enabling healthcare professionals and researchers to develop data-driven prediction models. The dataset classifies individuals as either likely or unlikely to have heart disease based on several patient attributes, including age, gender, chest pain type, blood pressure, cholesterol levels, and electrocardiogram results (https://archive.ics.uci.edu/dataset/45/heart+disease)²⁹. The overa....

Results

The experimental evaluation demonstrates that the CViHDKNN model achieves higher predictive performance than the CViHDDT model in heart disease classification. The CViHDKNN model achieved a test accuracy of 85.25%, whereas the CViHDDT model achieved 80.33%, indicating improved predictive capability for the distributed K-nearest neighbor approach. These comparative performance results are summarized in Table 1.

The classification performance of.......

Discussion

An effective heart disease prediction method based on the clustering technique CViHDDT was developed and evaluated using a Hadoop-based distributed decision tree framework. The model achieved a training accuracy of approximately 75.62% and a testing accuracy of 80.33%, demonstrating its ability to generalize to unseen data. The slightly higher test accuracy suggests that Hadoop’s parallel processing efficiently handles large datasets while minimizing overfitting. Previous studies have similarly reported that Hadoop.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Apache Spark	Apache Software Foundation	3.x	Distributed data processing
HDFS	Apache Software Foundation	Included in Hadoop 3.x	Distributed file storage
YARN	Apache Software Foundation	Included in Hadoop 3.x	Resource management and job scheduling
Matplotlib	Matplotlib Development Team		Data visualization
Seaborn	Seaborn Developers		Statistical plotting
Ubuntu OS	Canonical Ltd.	20.04 LTS	Operating system
RobustScaler	Scikit-learn		Feature normalization
UCI Heart Disease Dataset	UCI Machine Learning Repository	Dataset ID 45	Experimental dataset