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Title

Enhanced Heart Disease Prediction Using Machine Learning Techniques

  Jata Shanker Mishra 1 ,   N. K.Gupta 2 ,   Aditi Sharma 3 *

1  Department of Computer Science and Information Technology, Vaugh Institute of Agricultural Engineering And Technology, Prayagraj, U.P., India
    (jata.mishra@shiats.edu.in)

2  Department of Computer Science and Information Technology, Vaugh Institute of Agricultural Engineering And Technology, Prayagraj, U.P., India
    (narendra.gupta@shiats.edu.in)

3  Department of CSE, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India; SMIEEE, SIT, Symbiosis International (Deemed University), Pune, India
    (aditi.sharma@ieee.org)

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Doi   :   https://doi.org/10.54216/JISIoT.120202

Received: August 25, 2023 Revised: November 12, 2023 Accepted: April 09, 2024

Abstract :

This study leverages sophisticated machine learning methodologies, particularly XGBoost, to analyze cardiovascular diseases through cardiac datasets. The methodology encompasses meticulous data pre-processing, training of the XGBoost algorithm, and its performance evaluation using metrics such as accuracy, precision, and ROC curves. This technique represents a notable progression in the realm of medical research, potentially leading to enhanced diagnostic precision and a deeper comprehension of cardiovascular ailments, thereby improving patient care and treatment modalities in cardiology. Furthermore, the research delves into the utilization of deep learning methodologies for the automated delineation of cardiac structures in MRI and mammography images, aiming to boost diagnostic precision and patient management. [24][3][5][6] In assessing machine learning algorithms' efficacy in diagnosing cardiovascular diseases, this analysis underscores the pivotal role of such algorithms and their possible data inputs. Additionally, it investigates promising directions for future exploration, such as the application of reinforcement learning. A significant aspect of our investigation is the development and deployment of sophisticated deep learning models for segmenting right ventricular images from cardiac MRI scans, aiming at heightened accuracy and dependability in diagnostics. Through the utilization of advanced techniques like Fourier Convolutional Neural Network (FCNN) and improved versions of Vanilla Convolutional Neural Networks (Vanilla-CNN) and Residual Networks (ResNet), we achieved a substantial improvement in accuracy and reliability. This enhancement allows for more precise and quicker identification and diagnosis of cardiovascular diseases, which is of utmost importance in clinical practice. Evaluation of Machine Learning Algorithms: We conducted a comprehensive evaluation of machine learning algorithms in the context of cardiovascular disease diagnosis. This assessment emphasized the fundamental role of machine learning algorithms and their potential data sources. We also explored promising avenues, such as reinforcement learning, for future research. Factors Affecting Predictive Models: We highlighted the critical factors affecting the effectiveness of machine learning-based predictive models. These factors include data heterogeneity, depth, and breadth, as well as the nature of the modeling task, and the choice of algorithms and feature selection methods. Recognizing and addressing these factors are essential for building reliable models. 

Keywords :

XGBoost , Machine Learning , Cardiovascular Diseases , Cardiac Data Analysis , Data Preprocessing , Model Evaluation , Diagnostic Accuracy , ROC Curves , Medical Research , Cardiology

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Cite this Article as :
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MLA Jata Shanker Mishra, N. K.Gupta, Aditi Sharma. "Enhanced Heart Disease Prediction Using Machine Learning Techniques." Full Length Article, Vol. 12, No. 2, 2024 ,PP. 19-33 (Doi   :  https://doi.org/10.54216/JISIoT.120202)
APA Jata Shanker Mishra, N. K.Gupta, Aditi Sharma. (2024). Enhanced Heart Disease Prediction Using Machine Learning Techniques. Journal of Full Length Article, 12 ( 2 ), 19-33 (Doi   :  https://doi.org/10.54216/JISIoT.120202)
Chicago Jata Shanker Mishra, N. K.Gupta, Aditi Sharma. "Enhanced Heart Disease Prediction Using Machine Learning Techniques." Journal of Full Length Article, 12 no. 2 (2024): 19-33 (Doi   :  https://doi.org/10.54216/JISIoT.120202)
Harvard Jata Shanker Mishra, N. K.Gupta, Aditi Sharma. (2024). Enhanced Heart Disease Prediction Using Machine Learning Techniques. Journal of Full Length Article, 12 ( 2 ), 19-33 (Doi   :  https://doi.org/10.54216/JISIoT.120202)
Vancouver Jata Shanker Mishra, N. K.Gupta, Aditi Sharma. Enhanced Heart Disease Prediction Using Machine Learning Techniques. Journal of Full Length Article, (2024); 12 ( 2 ): 19-33 (Doi   :  https://doi.org/10.54216/JISIoT.120202)
IEEE Jata Shanker Mishra, N. K.Gupta, Aditi Sharma, Enhanced Heart Disease Prediction Using Machine Learning Techniques, Journal of Full Length Article, Vol. 12 , No. 2 , (2024) : 19-33 (Doi   :  https://doi.org/10.54216/JISIoT.120202)
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