Integrating Multimodal Data for Parkinson’s Disease Prediction: An Ensemble Approach
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3817-5_12
Reference6 articles.
1. Chakraborty S, Aich S, Jong-Seong-Sim, Han E, Park J, Kim H-C (2020) Parkinson's Disease detection from spiral and wave drawings using convolutional neural networks: a multistage classifier approach. In: 2020 22nd International conference on advanced communication technology (ICACT), Phoenix Park, Korea (South), pp 298–303. https://doi.org/10.23919/ICACT48636.2020.9061497
2. Aversano L, Bernardi ML, Cimitile M, Iammarino M, Verdone C (2022) Early detection of Parkinson’s disease using spiral test and echo state networks. In: 2022 international joint conference on neural networks (IJCNN), Padua, Italy, pp. 1–8. https://doi.org/10.1109/IJCNN55064.2022.9891917
3. Fiza S, Kumar A, Yadav J, Reddy S, Sidda TK, Sai K (2022) Classification of Parkinson's disease using CNN and ANN with the aid of drawing and acoustic feature. In: 2022 2nd International conference on advance computing and innovative technologies in engineering (ICACITE), Greater Noida, India, pp 513–517, https://doi.org/10.1109/ICACITE53722.2022.9823915
4. Tsanas A et al (2012) Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease. IEEE Trans Biomed Eng 59(5):1264–1271
5. Shirvan RA, Tahami E (2011) Voice analysis for detecting Parkinson's disease using genetic algorithm and KNN classification method. In: 2011 18th Iranian conference of biomedical engineering (ICBME). IEEE, pp 278–283
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