Editorial
An LSTM based DNN Model for Neurological Disease Prediction Using Voice Characteristics
@ARTICLE{10.4108/eetpht.10.5424, author={Anila M and G Kiran Kumar and D Malathi Rani and M V V Prasad Kantipudi and D Jayaram}, title={An LSTM based DNN Model for Neurological Disease Prediction Using Voice Characteristics}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={10}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2024}, month={3}, keywords={Voice Features, Deep Neural Network, LSTM, Parkinson's Diseases, Machine Learning ML}, doi={10.4108/eetpht.10.5424} }- Anila M
G Kiran Kumar
D Malathi Rani
M V V Prasad Kantipudi
D Jayaram
Year: 2024
An LSTM based DNN Model for Neurological Disease Prediction Using Voice Characteristics
PHAT
EAI
DOI: 10.4108/eetpht.10.5424
Abstract
INTRODUCTION: A neurological condition known as Parkinson's disease (PD); it affected millions of individuals worldwide. An early diagnosis can help enhance the quality of life for those who are affected with this disease. This paper presents a novel Deep neural network model based on Long Short-Term Memory (LSTM) design for the identification of PD using voice features. OBJECTIVES: This research work aims to Identify the presence of PD using voice features of individuals. To achieve this, a Deep neural Network with LSTM is to be designed. Objective of the work is to analyse the voice data and implement the model with good accuracy. METHODS: The proposed model is a Deep Neural Network with LSTM. RESULTS: The proposed method uses the features gleaned from voice signals for training phase of LSTM model which achieved an accuracy of 89.23%, precision value as 0.898, F1-score of 0.965, and recall value as 0.931and is observed as best when compared to existing models. CONCLUSION: Deep Neural Networks are more powerful than ANNs ahd when associated with LSTM , the model outperformed the job of identifying PD using voice data.
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