Identifying Multiple Diseases in the Human Body using Machine Learning

P. Nagaraj, V. Muneeswaran, B. Karthik Goud, K. Arjun, G. Vigneshwar Reddy, P. Girish Kumar Reddy
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Abstract

The main causes of death in India and around the world are chronic illnesses like heart disease, diabetes, and Parkinson’s disease. There is a need for potential treatments for chronic diseases because of its higher mortality rate than other diseases. The increase of medical data in healthcare domain and its accurate analysis are beneficial for early disease identification, patient treatment, and community services. Incorrect diagnosis increases the fatality. Thus, precise diagnosis tools for chronic diseases are required due to the high risk of diagnosis. Hence, to provide a promising solution with high accuracy, this study offers a unique diagnosis method based on machine learning. Several machine learning methods are being used in this study, and the algorithm for the prediction is chosen based on the model’s accuracy. The proposed model performs disease prediction with an accuracy of 87.66%.
使用机器学习识别人体多种疾病
在印度和世界各地,导致死亡的主要原因是心脏病、糖尿病和帕金森病等慢性病。由于慢性病的死亡率高于其他疾病,因此需要对其进行潜在的治疗。医疗卫生领域医疗数据的增加及其准确分析有利于疾病的早期识别、患者治疗和社区服务。错误的诊断增加了病死率。因此,由于慢性病的诊断风险高,需要精确的诊断工具。因此,为了提供一个有前景的高精度解决方案,本研究提供了一种独特的基于机器学习的诊断方法。在本研究中使用了几种机器学习方法,并根据模型的精度选择预测算法。该模型的疾病预测准确率为87.66%。
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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