Stratification of Parkinson Disease using python scikit-learn ML library

Abstract

Parkinson's disease is a disorder in the central nervous system which affects in the movement functions of the body. It is a chronic disease with the symptoms growing with time. It generally affects the older people when their symptoms gradually increase to a maximum. The disease can affect the basic functions of the body such as hearing, walking, talking etc. The analysis of this disease can be done with the help of generic machine learning algorithms which produce varying accuracies. Thus, the best one is chosen which will provide the highest accuracy in predicting if the disease is present in the patient or not. The dataset is taken from the UCI machine learning repository namely-Parkinson disease dataset with replicated acoustic features. There are 48 features present in the dataset pertaining to the disease for 240 patients. Various machine learning techniques that are utilized compared their efficiency in the classification. Thus, the best one is chosen with the highest accuracy since the applications in healthcare generally requires more accuracy and efficiencies cannot be compromised. The significant models that are used in this process are naaive bayes classifier, gradient boosting, support vector machines. These techniques can be very powerful for the doctors in order to predict the disease by analysing the features present in the patients.