Predicting the Parkinson’s disease using machine learning algorithms

Parkinson’s disease (PD) is a neurodegenerative movement disease where the symptoms gradually develop start with a slight tremor in one hand and a feeling of stiffness in the body and it became worse over time. Parkinson’s is considered one of the deadliest and progressive nervous system diseases that affect movement. It is the second most common neurological disorder that causes disability, reduces the life span, and still has no cure. Nearly 90% of affected people with this disease have speech disorders. In real-world applications, the information is been generated by using various Machine Learning techniques. Machine learning algorithms help to generate useful content from it. To increase the lifespan of elderly people the machine learning algorithms are used to detect diseases in the early stages. Speech features are the main concept while taking into consideration the term ‘Parkinson’s’. In this paper, we are using various Machine Learning techniques like Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree Classifier, Support Vector Machine (SVM), Bernoulli Naive Bayes(BNB), Gaussian Naive Bayes(GNB), Random Forest Classifier and how these algorithms are used to predict Parkinson’s based on the input taken from the user and the input for algorithms is the dataset. The data set contains 24 attributes and 195 instances. From the results, It shows the predicting accuracy of the algorithms. To recover the patients from early stages, prediction is important. This process can be done with the help of Machine Learning.