An LSTM based DNN Model for Neurological Disease Prediction Using Voice Characteristics

Authors

  • Anila M Chaitanya Bharathi Institute of Technology image/svg+xml
  • G Kiran Kumar Chaitanya Bharathi Institute of Technology image/svg+xml
  • D Malathi Rani Marri Laxman Reddy Institute of Technology and Management
  • M V V Prasad Kantipudi Symbiosis International University image/svg+xml
  • D Jayaram Chaitanya Bharathi Institute of Technology image/svg+xml

DOI:

https://doi.org/10.4108/eetpht.10.5424

Keywords:

Voice Features, Deep Neural Network, LSTM, Parkinson's Diseases, Machine Learning ML

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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References

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Published

14-03-2024

How to Cite

1.
M A, Kumar GK, Rani DM, Prasad Kantipudi MVV, Jayaram D. An LSTM based DNN Model for Neurological Disease Prediction Using Voice Characteristics . EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 14 [cited 2024 Apr. 25];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5424