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Classification and Prediction on Cardiovascular disease datasets

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Shiaofen FangAuthors Info & Claims

ICCDE '23: Proceedings of the 2023 9th International Conference on Computing and Data Engineering

Pages 15 - 21

https://doi.org/10.1145/3589845.3589852

Published: 22 June 2023 Publication History

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Abstract

Cardiovascular disease is the leading cause of death worldwide and in the U.S. Almost half of adults in the U.S. have some form of cardiovascular disease. It affects people of all ages, sexes, ethnicities and socioeconomic levels. However, people who have Cardiovascular diseases might be asymptomatic, which means the patient does not feeling anything at all. Asymptomatic patients would not get diagnosed until they reach a more serious stage and may miss the best time for treatment. The aim of this project is to collect data on cardiovascular disease, analyze the data and use them to build a predictive machine learning model for early-stage heart disease detection. Multiple different data pre-processing and classification methods have been applied and compared for the best prediction accuracy.

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Cited By

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Sattar SMumtaz RQadir MMumtaz SKhan MDe Waele TDe Poorter EMoerman IShahid A(2024)Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG DatasetsSensors10.3390/s2408248424:8(2484)Online publication date: 12-Apr-2024
https://doi.org/10.3390/s24082484

Index Terms

Classification and Prediction on Cardiovascular disease datasets
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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ICCDE '23: Proceedings of the 2023 9th International Conference on Computing and Data Engineering

January 2023

101 pages

ISBN:9781450398022

DOI:10.1145/3589845

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

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Published: 22 June 2023

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ICCDE 2023: 2023 9th International Conference on Computing and Data Engineering

January 6 - 8, 2023

Haikou, China

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Sattar SMumtaz RQadir MMumtaz SKhan MDe Waele TDe Poorter EMoerman IShahid A(2024)Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG DatasetsSensors10.3390/s2408248424:8(2484)Online publication date: 12-Apr-2024
https://doi.org/10.3390/s24082484

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Abstract

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Cited By

Index Terms

Recommendations

Prediction of CardioVascular Disease (CVD) using Ensemble Learning Algorithms

Ensemble framework for cardiovascular disease prediction

Cardiovascular Magnetic Resonance and Computed Tomography Imaging for the Assessment of Cardiovascular Complications of Type 2 Diabetes Mellitus

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