论文标题
性能，透明度和时间。特征选择以加快帕金森氏病的诊断
Performance, Transparency and Time. Feature selection to speed up the diagnosis of Parkinson's disease
论文作者
论文摘要
对疾病的准确和早期预测可以计划和改善患者未来生活的质量。在大流行状况中，医疗决定成为速度挑战，医师必须迅速采取行动诊断和预测疾病严重程度的风险，此外，这对于帕金森氏病等神经退行性疾病也很当然。具有特征选择（FS）技术的机器学习（ML）模型可以应用于帮助医生快速诊断疾病。 FS最佳子集功能，可改善模型性能并有助于减少患者所需的测试数量，从而加快诊断。这项研究显示了预先应用于分类器算法，逻辑回归，在非侵入性测试结果数据上的三种特征选择（FS）技术的结果。这三个FS是基于滤波器的方法的方差分析（ANOVA），最小绝对收缩和选择操作员（LASSO）作为嵌入式方法和顺序特征选择（SFS）作为包装方法。结果表明，FS技术可以帮助构建有效的分类器，从而改善分类器的性能，同时减少计算时间。
Accurate and early prediction of a disease allows to plan and improve a patient's quality of future life. During pandemic situations, the medical decision becomes a speed challenge in which physicians have to act fast to diagnose and predict the risk of the severity of the disease, moreover this is also of high priority for neurodegenerative diseases like Parkinson's disease. Machine Learning (ML) models with Features Selection (FS) techniques can be applied to help physicians to quickly diagnose a disease. FS optimally subset features that improve a model performance and help reduce the number of needed tests for a patient and hence speeding up the diagnosis. This study shows the result of three Feature Selection (FS) techniques pre-applied to a classifier algorithm, Logistic Regression, on non-invasive test results data. The three FS are Analysis of Variance (ANOVA) as filter based method, Least Absolute Shrinkage and Selection Operator (LASSO) as embedded method and Sequential Feature Selection (SFS) as wrapper method. The outcome shows that FS technique can help to build an efficient and effective classifier, hence improving the performance of the classifier while reducing the computation time.
