Parkinson’s Disease Detection Analysis through Machine Learning Approaches

Abstract

Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a diseases for our senior citizen in Bangladesh. Parkinson's Disease indications often seem progressively and got worst with time. People got effected have trouble walking and communicating with the condition advances. Patients can also psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though, men are affected by the illness at a proportion that is around partial of them are women. In this regarding research we have to get out the accurate ML algorithm to find out the disease with predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifier are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier and Gradient Boosting Classifier are used.