Heart Disease Prediction System using Machine Learning Model

P. S. Tomar, Kalpana Rai, Mohd. Zuber
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Abstract

Computer-based computation play a tremendous role over the last two decades, as the demand for computers and technology is growing day by day, Data science is a technique to handle the large amount of data which is generated from the computer and other computational-based devices, the data is playing very crucial role nowadays. Artificial intelligence techniques can handle a large amount of data in various filed, like computer-based vision, medical imaging, object detection and tracking, security surveillance, and other fields. The healthcare sector is one of the most promising and necessity-based sectors among the other sectors, therefore machine learning-based models provide all the required computation and solutions for the computer-aided disease diagnosis. In this work we present the comparative machine learning model to predict heart disease on basis of different features and, also improve the accuracy and some other performance parameters to detect the respective diseases. Our experimental result shows better accuracy and other performance parameters value than existing techniques.
基于机器学习模型的心脏病预测系统
基于计算机的计算在过去的二十年中发挥了巨大的作用,随着对计算机和技术的需求日益增长,数据科学是一门处理从计算机和其他基于计算的设备产生的大量数据的技术,数据在当今起着至关重要的作用。人工智能技术可以处理各种领域的大量数据,如计算机视觉、医学成像、物体检测和跟踪、安全监控等领域。医疗保健行业是最具发展前景和需求的行业之一,因此基于机器学习的模型为计算机辅助疾病诊断提供了所需的所有计算和解决方案。在这项工作中,我们提出了基于不同特征的比较机器学习模型来预测心脏病,并提高了准确率和其他一些性能参数来检测各自的疾病。实验结果表明,与现有技术相比,我们的精度和其他性能参数值都有所提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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