Predicting Parkinson’s Disease: Python Machine Learning

Abstract

Parkinson's disease is a progressive neurological disease that affects a large number of people and has a significant impact on their quality of life. Human motor function is mostly affected by this condition. "Parkinsonism" or "Parkinsonian" syndrome refers to the primary motor symptoms. Parkinson's disease causes tremors, rigidity, slowness of movement, and trouble walking, as well as changes in thinking and behaviour. Anxiety and despair are frequent as well. It occurs as a result of a decrease in dopamine levels in the brain. There is a model for identifying Parkinson's disease using speech that was developed in 2015. The deflection in the voice of patient confirms the symptoms of PD. The efficiency of this project was 73.8 percent. A large amount of data is collected from normal people who have Parkinson's disease, and this data is trained using a machine learning method in our model. Sixty percent of the data is used for training, and the remaining forty percent is used for testing. Any person's data can be entered into the database to determine whether or not they are impacted by Parkinson's disease. Except for the status column, the data set has 24 columns, each of which will display a patient's symptoms values. The status column has 0’s and 1’s. Those value will decide the persons is affected with Parkinson’s disease. 1’s indicate persons is affected, 0’s indicates normal conditions with the accuracy of more than 94%.