基于支持向量机和随机森林分类器的特发性帕金森病预后分析

Q3 Pharmacology, Toxicology and Pharmaceutics
Raghavendra M Devadas, Vani Ashok Hiremani
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引用次数: 1

摘要

世界各地的许多人都患有特发性帕金森病,这种疾病在50岁以上的人中更为常见。即使在今天,尽管有许多技术发展和突破,早期疾病识别仍然很困难。这就需要开发基于机器学习的自动方法,帮助医生在早期准确识别这种疾病。本文的主要目标是对当前用于IP检测的机器学习方法进行深入的分析和比较。为了比较和确定两种分类器中哪一种对IP分类最有效、最准确,本文在数据集上讨论了支持向量机和随机森林。支持向量机的准确率和Kappa评分分别为85.6%和0.814。在随机森林中发现准确率为86.45%,kappa评分为0.81。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prognosis Of Idiopathic Parkinsonism Using Support Vector Machine And Random Forest Classifiers
Many of individuals all over the world suffer with Idiopathic Parkinsonism (IP) which is more common in people over 50. Even today, despite numerous technological developments and breakthroughs, early disease identification is still difficult. This calls for the development of machine learning-based automatic methods that assist doctors in accurately identifying this disease in its early stages. This research paper's main goal is to give a thorough analysis of and comparison of the current machine learning methods used for IP detection. In order to compare and determine which of the two classifiers is the most effective and accurate for classifying IP, this paper discusses Support Vector Machine and Random Forest on a dataset. Accuracy and Kappa scores for support vector machine is 85.6% and 0.814. Accuracy 86.45% and kappa score of 0.81 was found in random forest.
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