Machine Learning and Neural Networks for IT-Diagnostics of Neurological Diseases

Abstract

The article considers machine learning methods and neural networks for diagnosing neurological diseases (Alzheimer’s and Parkinson’s diseases) in patients based on voice analysis. Models of information about disease features (including frequency, jitter, mel-cepstral coefficients, etc.) extracted from voice data are presented. Various classifiers are used to train neural networks and recognize diseases. Among them are the GridSearchCV algorithm for optimizing the hyperparameters of the random forest classifier for recognizing Alzheimer’s disease (recognition accuracy is 87.6 %) and the KNN algorithm for training and testing on publicly available datasets of speech change features in patients with Parkinson’s disease. The KNN algorithm showed the best classification results compared to others, achieving an experimental accuracy of 94 % on the same datasets. It is noted that the use of multidimensional feature extraction and machine learning methods can improve the accuracy of early diagnosis of neurological diseases.