Detection of Parkinson’s Disease Using XGBOOST Algorithm

Abstract: PREDICTING PARKINSON’S DISEASE using XGBOOST ALGORITHM. In this project we will be using python to build a model using which we can accurately detect the presence of Parkinson’s disease in one’s body. XGBOOST algorithm is a technique for regression and classification problems. It produces a prediction model in form of a decision tree. Data is loaded, features and label are specified, data is split, XGBClassifier is produced and calculate the accuracy of our model. Parkinson’s disease is caused by the disruption of the brain cells that produce a substance to allow brain cells to communicate with each other, called dopamine. The cells that produce dopamine in the brain are responsible for the control, adaptation, and fluency of movements. When 60–80%of these cells are lost, then enough dopamine is not produced and Parkinson’s motor symptoms appear. It is thought that the disease begins many years before the motor (movement-related) symptoms and therefore, researchers are looking for ways to recognize the non-motor symptoms that appear early in the disease as early as possible, thereby halting the progression of the disease. In this project, diagnosis of Parkinson’s disease is presented. Using the python libraries scikit-learn, NumPy, pandas, and XGBoost, we will build a model using an XGBClassifier. We’ll load the data, get the features and labels, scale the features, then split the dataset, build an XGBClassifier, and then calculate the accuracy of our model. 94.87% accuracy was achieved with the least number of voice features for Parkinson’s diagnosis