Machine Learning-Based Approach for Predictive Analytics in Healthcare

S. Hegde, Monica R. Mundada
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引用次数: 1

Abstract

In this internet era, due to digitization in every application, a huge amount of data is produced digitally from the healthcare sectors. As per the World Health Organization (WHO), the mortality rate due to the various chronic diseases is increasing each day. Every year these diseases are taking lives of at least 50 million people globally, which includes even premature deaths. These days, machine learning (ML)-based predictive analytics are turning out as effective tools in the healthcare sectors. These techniques can extract meaningful insights from the medical data to analyze the future trend. By predicting the risk of diseases at the preliminary stage, the mortality rate can be reduced, and at the same time, the expensive healthcare cost can be eliminated. The chapter aims to briefly provide the domain knowledge on chronic diseases, the biological correlation between theses disease, and more importantly, to explain the application of ML algorithm-based predictive analytics in the healthcare sectors for the early prediction of chronic diseases.
基于机器学习的医疗保健预测分析方法
在这个互联网时代,由于每个应用程序的数字化,医疗保健部门产生了大量的数字化数据。根据世界卫生组织(卫生组织)的资料,各种慢性疾病造成的死亡率每天都在增加。这些疾病每年在全球夺走至少5 000万人的生命,其中甚至包括过早死亡。如今,基于机器学习(ML)的预测分析正在成为医疗保健行业的有效工具。这些技术可以从医疗数据中提取有意义的见解来分析未来的趋势。通过在早期阶段预测疾病的风险,可以降低死亡率,同时可以消除昂贵的医疗费用。本章旨在简要介绍慢性病的领域知识,以及这些疾病之间的生物学相关性,更重要的是,解释基于ML算法的预测分析在医疗保健领域的应用,以早期预测慢性病。
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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