A Case Study of Parkinsons Disease Diagnosis using Artificial Neural Networks

Artificial Neural Network (ANN)-based diagnosis of medical diseases has been taken into great consideration in recent years. In this paper, two types of ANNs are used to classify effective diagnosis of Parkinson's disease. Multi-Layer Perceptron (MLP) with back-propagation learning algorithm and Radial Basis Function (RBF) ANNs were used to differentiate between clinical variables of samples (N = 195) who were suffering from Parkinson's disease and who were not. For this purpose, Parkinson's disease data set, taken from UCI machine learning database was used. Mean squared normalized error function was used to measure the usefulness of our networks during trainings and direct performance calculations. It was observed that MLP is the best classi?cation with 93. 22% accuracy for the data set. Also, we got 86. 44% accuracy in RBF classification for the same data set. This technique can assist neurologists to make their ultimate decisions without hesitation and more astutely.