Parkinson’s Disease Prediction using Adaptive Quantum Computing
Abstract
Adaptability is the most generous thing we need to acquire to solve any kind of prediction model design and implementation. Dementia is the most dangerous disease which will affect the human nervous system. Parkinson's is one of the most occupied space in dementia. It will affect complete operational behavior of the patient. Using machine learning and the quantum computing the proposed system is working on implementing the speech signal-based implementation on the Parkinson's disease prediction. The prediction involves the four major algorithms of the machine learning like Naïve Bayes, K-NN, Decision trees and Artificial Neural Networks. Some of the ensemble learning models in machine learning used for the increment of the accuracy of the models by combining several combinations of the models. The performance of the model will be decided using the standard dataset from UCI machine learning repository. The ensemble models overcome the accuracy of the most accurate method like neural networks. The proposed system consists of the multi-layer perceptron which is one of the most relevant optimization methods in the machine learning.
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