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    International Journal of Innovative Science and Research Technology
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    Heart disease remains one of the leading causes of mortality worldwide, making early detection and prevention crucial. Machine learning techniques offer promising avenues for predicting heart attack possibilities by analyzing patient data and identifying risk factors. This study explores the development of a predictive model using machine learning algorithms to assess the likelihood of a heart attack based on individual patient characteristics and medical history.

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    Heart disease remains one of the leading causes of mortality worldwide, making early detection and prevention crucial. Machine learning techniques offer promising avenues for predicting heart attack possibilities by analyzing patient data and identifying risk factors. This study explores the development of a predictive model using machine learning algorithms to assess the likelihood of a heart attack based on individual patient characteristics and medical history.

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    7 views2 pages

    Predict The Heart Attack Possibilities Using Machine Learning

    Uploaded by

    International Journal of Innovative Science and Research Technology
    Heart disease remains one of the leading causes of mortality worldwide, making early detection and prevention crucial. Machine learning techniques offer promising avenues for predicting heart attack possibilities by analyzing patient data and identifying risk factors. This study explores the development of a predictive model using machine learning algorithms to assess the likelihood of a heart attack based on individual patient characteristics and medical history.

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    Volume 9, Issue 4, April – 2024 International Journal of Innovative Science and Research Technology

    ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/IJISRT24APR1878

    Predict the Heart Attack Possibilities Using


    Machine Learning
    Pratik Bodake1; Akash Shevkar 2; Jaydeep Padwal3; Yogeshwari Hardas4
    1,2,3,4
    Student, Dept of Computer Engineering, Atma Malik Institute of Technology and Research, Maharashtra, India
    4
    Prof ., Head of Department of Computer Engineering, Atma Malik Institute of Technology and Research, Maharashtra, India

    Abstract:- Heart disease remains one of the leading II. LITERATURE SURVEY
    causes of mortality worldwide, making early detection
    and prevention crucial. Machine learning techniques [1] Traditional Risk Factors and Beyond: Early studies
    offer promising avenues for predicting heart attack often focused on traditional risk factors such as age, gender,
    possibilities by analyzing patient data and identifying hypertension, and cholesterol levels. However, more recent
    risk factors. This study explores the development of a research has expanded to include novel predictors such as
    predictive model using machine learning algorithms to genetic markers, lifestyle factors, psychosocial variables,
    assess the likelihood of a heart attack based on and emerging biomarkers like high-sensitivity C-reactive
    individual patient characteristics and medical history. protein (hs-CRP) and homocysteine levels.

    The dataset comprises a comprehensive range of [2] Datasets and Cohorts: Researchers have utilized
    features including demographic information, lifestyle various datasets and cohorts for heart attack prediction,
    factors, medical history, and results from diagnostic tests including longitudinal studies like the Framingham Heart
    such as electrocardiograms (ECG), cholesterol levels, Study, the UK Biobank, and electronic health records (EHR)
    and blood pressure readings. Preprocessing techniques databases from healthcare institutions. These datasets
    such as data cleaning, normalization, and feature provide rich sources of information for training and
    engineering are applied to prepare the dataset for validating machine learning models.
    analysis. Looking ahead, the article identifies promising
    avenues for future research, including the integration of [3] Feature Engineering and Selection: Feature
    multimodal data sources, real-time risk assessment engineering plays a crucial role in extracting relevant
    systems, and collaborative efforts to develop information from raw data. Studies have explored different
    standardized benchmarks and evaluation protocols. By techniques for feature selection, dimensionality reduction,
    synthesizing the collective knowledge gleaned from and handling missing values to enhance model performance
    decades of research, this historical review aims to inform and interpretability.
    and inspire ongoing endeavors in leveraging machine
    learning for proactive cardiovascular health [4] According to Krittanawong, Chayakrit, et al.
    management and prevention strategies. "Artificial intelligence in precision cardiovascular
    medicine." Journal of the American College of Cardiology
    Keywords:- Support Vector Machine ,Machine Learning 2017.
    Algorithm, Computational Modeling.
    [5] According to Motwani, Manish, et al. "Machine
    I. INTRODUCTION learning for prediction of all-cause mortality in patients with
    suspected coronary artery disease: a 5-year multicentre
    Heart disease remains a significant global health prospective registry analysis." European heart journal 2017
    concern, responsible for a substantial portion of mortality
    and morbidity worldwide. Among the various [6] According to Choi, Eunho, et al. "Cardiovascular
    cardiovascular conditions, heart attacks, or myocardial disease prediction using deep learning techniques: A
    infarctions, pose a particularly grave threat due to their review." In 2016 IEEE International Conference on
    sudden onset and potentially life-threatening consequences. Healthcare Informatics (ICHI), pp. 209-215. IEEE, 2016
    Early identification of individuals at risk of experiencing a
    heart attack is paramount for implementing preventive
    measures and timely interventions to mitigate adverse
    outcomes.

    IJISRT24APR1878 www.ijisrt.com 1193


    Volume 9, Issue 4, April – 2024 International Journal of Innovative Science and Research Technology
    ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/IJISRT24APR1878

    III. PROPOSED SYSTEM IV. CONCLUSION

    The process typically includes steps such as data The proposed system represents a comprehensive
    preprocessing, feature selection, model training, evaluation, approach to predicting heart attack possibilities using
    and validation. machine learning, leveraging advanced computational
    techniques and interdisciplinary collaboration to enhance
    The resulting predictive models can assist healthcare cardiovascular risk assessment and preventive care. By
    providers in identifying individuals at high risk of a heart harnessing the power of data-driven insights, the system
    attack, enabling proactive interventions such as lifestyle aims to improve patient outcomes, reduce healthcare costs,
    modifications, medication adjustments, or referral to and alleviate the burden of heart disease on individuals and
    specialized care. healthcare systems worldwide.

    Overall, predicting heart attack possibilities using REFERENCES


    machine learning holds the potential to improve early
    detection, optimize preventive strategies, and ultimately [1]. Fatima M, Pasha M: Survey of machine learning
    reduce the incidence and severity of heart disease. algorithms for disease diagnostic. J. Intell. Learn. Syst.
    Appl. 2017; 09: 1–16. Publisher Full Text
    [2]. Singh RS, Saini BS, Sunkaria RK: Detection of
    coronary artery disease by reduced features and
    extreme learning machine. Med. Pharm. Rep. 2018;
    91(2): 166–175. PubMed Abstract | Publisher Full Text
    [3]. Yaghouby F, Ayatollahi A, Soleimani R: Classification
    of cardiac abnormalities using reduced features of heart
    rate variability signal. World Appl. Sci. J. 2009; 6(11):
    1547–1554.
    [4]. Asl BM, Setarehdan SK, Mohebbi M: Support vector
    machine-based arrhythmia classification using reduced
    features of heart rate variability signal. Artif. Intell.
    Med. 2008; 44(1): 51–64. PubMed Abstract | Publisher
    Full Text.

    Fig 1 Block Diagram of the Proposed System

    IJISRT24APR1878 www.ijisrt.com 1194

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