research-article

Statistic Solution for Machine Learning to Analyze Heart Disease Data

Authors:

Promise Agbedanu,

Neeta ChoudhryAuthors Info & Claims

ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and Computing

Pages 134 - 139

https://doi.org/10.1145/3383972.3384061

Published: 26 May 2020 Publication History

Abstract

Data crawling, collection and analysis have become a popular pillar for the business intelligence of big data analysis which is the latest hot-topic among the research association. Numerous tools and techniques to solve and analyze the structured and unstructured datasets are developing very quickly. The previous studies show the different approaches in the identification of the strengths and weaknesses of multiple machine learning algorithms. But, most of the approaches demand more expert knowledge base information to understand the concepts of given data. In this paper, we modernize the machine learning methods for the effective prediction of heart disease. This work deliberates the detailed process of implementation of our proposed system. The goal of this work is to find a strong and effective machine learning algorithm for disease prediction for the problem; how can doctors get fast and better results for their diagnosis of heart disease. We design a new system for disease prediction using machine learning prediction algorithms (LR, ANN and SVC) by utilizing an effective approach of ETL, OLAP and data mining. The results showed that the best machine learning algorithm is SVC with 92% accuracy for the risk prediction model. We found that subjects at 56-64 years old have a high risk of heart disease, as well as men, have more heart disease rate than women. This proposed study can be favorable for the medical practitioners in the field of healthcare, supportive practice and precautions to the heart disease patients.

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Cited By

Vinodhini VSathiyabhama BVidhushavarshini SRavi VShashidhar R(2025)Ensemble-based Heart Disease Diagnosis (EHDD) Using Feature Selection and PCA Extraction MethodsThe Open Bioinformatics Journal10.2174/011875036234930525013106251018:1Online publication date: 6-Feb-2025
https://doi.org/10.2174/0118750362349305250131062510
Mawlood DFranci DHamad DKareem S(2024)Enhanced machine learning models for predicting breast cancer: Healthcare systemITM Web of Conferences10.1051/itmconf/2024640102064(01020)Online publication date: 5-Jul-2024
https://doi.org/10.1051/itmconf/20246401020
Magboo VMagboo M(2023)Cardiovascular disease prediction with imputation techniques and recursive feature eliminationTHE 6TH INTERNATIONAL CONFERENCE ON ENERGY, ENVIRONMENT, EPIDEMIOLOGY AND INFORMATION SYSTEM (ICENIS) 2021: Topic of Energy, Environment, Epidemiology, and Information System10.1063/5.0124079(030016)Online publication date: 2023
https://doi.org/10.1063/5.0124079
Show More Cited By

Index Terms

Statistic Solution for Machine Learning to Analyze Heart Disease Data
1. Information systems
  1. Information systems applications
    1. Mobile information processing systems

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ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and Computing

February 2020

607 pages

ISBN:9781450376426

DOI:10.1145/3383972

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shenzhen University: Shenzhen University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 May 2020

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ICMLC 2020

ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing

February 15 - 17, 2020

Shenzhen, China

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Cited By

Vinodhini VSathiyabhama BVidhushavarshini SRavi VShashidhar R(2025)Ensemble-based Heart Disease Diagnosis (EHDD) Using Feature Selection and PCA Extraction MethodsThe Open Bioinformatics Journal10.2174/011875036234930525013106251018:1Online publication date: 6-Feb-2025
https://doi.org/10.2174/0118750362349305250131062510
Mawlood DFranci DHamad DKareem S(2024)Enhanced machine learning models for predicting breast cancer: Healthcare systemITM Web of Conferences10.1051/itmconf/2024640102064(01020)Online publication date: 5-Jul-2024
https://doi.org/10.1051/itmconf/20246401020
Magboo VMagboo M(2023)Cardiovascular disease prediction with imputation techniques and recursive feature eliminationTHE 6TH INTERNATIONAL CONFERENCE ON ENERGY, ENVIRONMENT, EPIDEMIOLOGY AND INFORMATION SYSTEM (ICENIS) 2021: Topic of Energy, Environment, Epidemiology, and Information System10.1063/5.0124079(030016)Online publication date: 2023
https://doi.org/10.1063/5.0124079
Rasool ABunterngchit CTiejian LIslam MQu QJiang Q(2022)Improved Machine Learning-Based Predictive Models for Breast Cancer DiagnosisInternational Journal of Environmental Research and Public Health10.3390/ijerph1906321119:6(3211)Online publication date: 9-Mar-2022
https://doi.org/10.3390/ijerph19063211
Tran TLe UShi Y(2022)An effective up-sampling approach for breast cancer prediction with imbalanced data: A machine learning model-based comparative analysisPLOS ONE10.1371/journal.pone.026913517:5(e0269135)Online publication date: 27-May-2022
https://doi.org/10.1371/journal.pone.0269135
Manzoor APan QKhan HSiddeeq SBhatti HWedagu M(2020)Analysis and Detection of Lung Sounds Anomalies Based on NMA-RNN2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM49941.2020.9313197(2498-2504)Online publication date: 16-Dec-2020
https://doi.org/10.1109/BIBM49941.2020.9313197

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