Computer Aided Diagnosis (CAD) of Parkinson‘s Disease with Machine Learning Models

Abstract

Parkinson‘s disease is a disorder of nervous system that mainly affects aged people. It is caused by the progressive degeneration of nerve cells in the brain, which results in a lack of dopamine necessary for controlled movements. As Parkinson‘s disease can not be cured, the early detection of Parkinson‘s disease is very helpful for a better management and care of patients, their families and the communities. This thesis reviewed the research status of Parkinson’s Disease (PD) diagnosis and extensive genetic association of PD issues, and proposed a key feature based machine learning strategy for PD classification tasks based on the open source database PPMI. In this thesis, a series of classification experiments were carried out, such that the set of single modality models were compared to a multiple modality model for PD diagnosis. Results of these experiments lead to a critical issue that more features for a diagnostic system seem to be necessary, but it should be verified if the more features are, the better the performance of a diagnostic system is. Therefore, a key feature classification strategy was proposed to explore the issue proposed above. At the first step, diagnostic features for PD classification are ranked by a decision tree model. This ranking procedure is then followed by a feeding procedure, in which a set of selected key features according to their importance ranking are fed into a machine learning model progressively, each time by feeding one more feature. This feeding procedure is iteratively processed until the performance reaches a local or global optimum. Based on the key feature classification strategy, an extensive investigation was carried out for the purpose to detect genetic association of PD. Results indicate that the proposed key feature based methodology leads to a more effective classification by a variety of machine learning models for PD diagnosis. The results presented in this thesis show that it is possible to conduct fewer key clinical examinations for PD diagnosis, or several conventional clinical examinations without expensive genetic sequencing studies to detect genetic associations to PD from other PD related categories successfully. The findings in this thesis have both social and economical values.