Parkinson and essential tremor classification to identify the patient?s risk based on tremor severity

Parkinson's disease (PSD) and essential tremor (ET) are oscillatory and rhythmic movements in the human body with similar characteristics and becomes challenging to identify it accurately. Thus, the chances of misdiagnosis are high. Researchers employed machine learning (ML) algorithms to accurately classify ET and PSD patients. This requires manual feature extraction that, without knowing their importance in prediction purposes, can be mitigated with automated feature engineering using deep learning (DL). So, in this paper, we propose a convolutional neural network (CNN)-based classification model with seven hidden layers and different filter sizes for the accurate classification of PSD and healthy control (HC) subjects. A flatten layer converts three-dimensional data to one-dimensional Tensor flow. Finally, the dense layer outputs the classification of PSD and HC patients based on tremor intensity to identify the PSD patient's risk at an early stage. It outperforms the traditional models with 92.4% accuracy of tremor classification.

Automated summary

In this study, a classification model based on convolutional neural network and machine learning algorithms was proposed to accurately identify Parkinson's disease and essential tremor patients. Through automated feature engineering, the model outperformed traditional methods in tremor classification.