OHE2LM: A Hybrid Approach Towards Heart Attack Prediction using One-Hot Encoding based Extreme Learning Machine Model

Main Article Content

Pawan Kumar Mall, Swapnita Srivastava, Mitul M. Patel, Aniruddh Kumar, Vipul Narayan, Sanjay Kumar, P. K. Singh, D. S. Singh

Abstract

Predicting heart attacks stands as a significant concern contributing to global morbidity. Within clinical data analysis, cardiovascular disease emerges as a pivotal focus for forecasting, wherein Data Science and machine learning (ML) offer invaluable tools. These methodologies aid in predicting heart attacks by considering various risk factors Just like high blood pressure, increased cholesterol levels, irregular pulse rates, and diabetes, this research aims to enhance the accuracy of predicting heart disease through machine learning techniques.This study introduces a MLdriven approach, termed ML-ELM, dedicated to forecasting heart attacks by analysing diverse risk factors. The proposed ML-ELM model is compared with alternative Utilizing machine learning techniques like Support Vector Machines, Logistic Regression, Naïve Bayes, and XGBoost is a key aspect of this exploration into different approaches for predictive modeling., is part of the research strategy. The dataset utilized for heart disease symptoms is sourced from the UCI ML Repository. The outcomes reveal that our proposed ML-ELM model has demonstrated superior predictive performance among the ML techniques tested. ML models show notable efficiency in identifying heart attack symptoms, particularly with boosting algorithms. Accuracy assessments were employed to gauge the predictive ability, Our suggested model demonstrated an outstanding accuracy rate of 96.77%.

Article Details

How to Cite
Pawan Kumar Mall, et al. (2023). OHE2LM: A Hybrid Approach Towards Heart Attack Prediction using One-Hot Encoding based Extreme Learning Machine Model. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 1961–1968. https://doi.org/10.17762/ijritcc.v11i10.8808
Section
Articles
Author Biography

Pawan Kumar Mall, Swapnita Srivastava, Mitul M. Patel, Aniruddh Kumar, Vipul Narayan, Sanjay Kumar, P. K. Singh, D. S. Singh

Pawan Kumar Mall1, Swapnita Srivastava2, Mitul M. Patel3, Aniruddh Kumar4, Vipul Narayan5, Sanjay Kumar6, P. K. Singh7, D. S. Singh8

1Assistant Professor, GL Bajaj Institute of Technology and Management

pawankumar.mall@gmail.com

2Assistant Professor, GL Bajaj Institute of Technology and Management

swapnitasrivastava@gmail.com

3Assistant Professor, Department of Electronics and Communication Engineering, Parul Institute of Engineering and Technology,

Parul University, Vadodara, India;

patelmitul4388@gmail.com

4Department of Computer Science and Engineering, Galgotias College of Engineering and Technology

Knowledge Park-2 Greater Noida

aniruddh.knit@gmail.com

5Assistant Professor, Galgotias University

Gautam Buddha Nagar, Uttar Pradesh

vipulupsainian2470@gmail.com

6Assistant Professor, Rajkiya Engineering College Azamgarh

sanjay@gecazamgarh.ac.in

7Professor, Computer Science and Engineering Department, Madan Mohan Malaviya University Of Technology, Gorakhpur, 273010, Uttar Pradesh, India.

topksingh@gmail.com

8Associate Professor, Computer Science and Engineering Department, Madan Mohan Malaviya University 0f Technology, Gorakhpur, 273010, Uttar Pradesh, India.

dss_mec@yahoo.co