论文标题
一项关于机器学习方法的全面研究，以提高分类器的预测准确性并减少诊断阿尔茨海默氏病所需的医疗测试数量
A Comprehensive Study on Machine Learning Methods to Increase the Prediction Accuracy of Classifiers and Reduce the Number of Medical Tests Required to Diagnose Alzheimer'S Disease
论文作者
论文摘要
阿尔茨海默氏症的患者逐渐失去了思考，表现和与他人互动的能力。病史，实验室测试，日常活动和人格变化都可以用来诊断疾病。一系列耗时且昂贵的测试用于诊断疾病。鉴定阿尔茨海默氏病的最有效方法是在本研究中使用随机森林分类器，以及其他各种机器学习技术。这项研究的主要目的是微调分类器以通过更少的测试来检测疾病，同时保持合理的疾病发现准确性。我们使用30个经常使用的指标成功地在近94％的情况下成功识别了这种情况。
Alzheimer's patients gradually lose their ability to think, behave, and interact with others. Medical history, laboratory tests, daily activities, and personality changes can all be used to diagnose the disorder. A series of time-consuming and expensive tests are used to diagnose the illness. The most effective way to identify Alzheimer's disease is using a Random-forest classifier in this study, along with various other Machine Learning techniques. The main goal of this study is to fine-tune the classifier to detect illness with fewer tests while maintaining a reasonable disease discovery accuracy. We successfully identified the condition in almost 94% of cases using four of the thirty frequently utilized indicators.
