Design And Development Of AnAlgorithm To Detect And Diagnose Parkinson’sDisease
Parkinson’s disease is a neuro degenerative disorder that severely deteriorates the brain cells and leads to trembling , rigidity, and toughness in movement, coordination, and balance. Symptoms of this disease gets worse with time and will also lead to amnesia, a decline in mental health, tiredness, and a change in the sleeppattern. The impairment or death of the locomotor-controllingneurons present in our brains is the major cause of this disease.Machinelearningtechniquesandalgorithmsplayaveryimportant role in recognizing the patterns in the medical science field.These techniques have aided many researchers in categorizing medical images and forecasting the models to have a better understanding of complex medical problems.Multiple kinds of research have been carried out int his field using various classifiers and algorithms,which results out in prediction accuracy ranging from 60percentto99percent.Inthispaper,theXGBoost algorithm is being used for the categorization of patientdatasets to predict the people affected by the disease and normal healthy people.A thorough data cleaning, extraction of the feature,and data analysis are implemented before using this algorithm. A successful classification of the people affected by Parkinson’s disease and normal people is done using the XG Boost algorithm to obtain high accuracy. This helps in understanding that this algorithm can derive the most discriminative featuresfromclinicaldata.