Machine Learning Approach for Parkinson's disease Prediction through Quantum Computing Techniques
Abstract
One of the most serious illnesses that may impact the neurological system in humans is dementia, of which Parkinson's disease is a major subtype. Patients with Parkinson's disease have significant behavioural changes. The suggested system uses quantum computing and machine learning to build a speech signal-based method for Parkinson's disease prediction. Four main machine learning methods are used in this prediction: Artificial Neural Networks, KNN, Decision Trees, and Naïve Bayes. Furthermore, different ensemble learning models are used to combine numerous models and improve the accuracy. An industry standard dataset from the UCI Machine Learning Repository is used to assess the model's performance. Ensemble models have proven to outperform extremely efficient techniques like neural networks in terms of accuracy. A multilayer perceptron, a very significant machine learning optimization technique, is incorporated into the suggested system.
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