Decision Making in Health Care Diagnosis: Evidence From Parkinson's Disease Via Hybrid Machine Learning
本文展示了如何用机器学习分类器，特别是改进的人工神经网络，通过非临床数据（如语音）早期诊断帕金森病，准确率比传统方法提高13.4%，有助于减少诊断失误。
Health care is a complex system that demands critical decision making, especially in the diagnosis of various conditions in patients. To minimize possible errors in diagnosis, an emerging technology, machine learning (ML), is being effectively used. ML classifiers can be used to proactively diagnose the medical conditions, which are identified based on the presence or absence of specific characteristics of the diseases. Therefore, in this article, we demonstrate how ML can be used to determine Parkinson's disease (PD) and thereby, provide early diagnosis using nonclinical data of the patients. Novel ensembles are developed in this article to improve the diagnostic capability and the experimental results show that the improved versions of artificial neural network (ANN) could yield 13.4% more accurate results compared with the traditional ANN classifier. PD is considered a challenging medical condition, owing to its global relevance and complexity in diagnosis. Moreover, the early detection of PD is instrumental for patient recovery, and any lapses in diagnosis can lead to an immeasurable loss to patients. Also, the study has developed an effective diagnostic tool for PD and detects the disease at an early stage using the voice data of individuals, and this will aid in making better clinical decisions related to PD, thus rendering better health services.