Fuzzy KNN Implementation for Early Parkinson's Disease Prediction

Abstract

Most machine learning models have had tremendous success in implementing prediction analysis on dePD end diseases such as brain tumors, making it an ambitious goal to apply machine learning to medical research discoveries. In the case of Parkinson's disease, for example, early diagnosis and understanding might allow patients to adopt preventative measures before the onset of clinical symptoms. In cases when no effective therapies exist, machine learning mPDe provides a means of making an early diagnosis and thereby improving patient outcomes. A loss of brain function, like in the case of Dementia, impairs the ability of the rest of the body to function normally. In medicine, the application of machine learning models is known as “quantum intelligence,” and it is used to determine which drug combinations work best. In terms of technology and automation, quantum computing or intelligence is among the highest. We're attempting to apply quantum intelligence to a dataset about Parkinson's disease. Models of machine learning and deep learning, such as random forests, decision trees, convolutional neural networks, recurrent neural networks, and so on, are applied to a variety of generic datasets. OASIS is a specialized neuroimaging dataset with a variety of MRI patient dimensions. This can be incorporated into future studies. We are employing 3D brain pictures to diagnose the tumour using machine learning models, and MRI has multiple facets to be taken into account before arriving at a diagnosis. Magnetic Resonance Imaging (MRI) has two variations, known as T1 and T2. When it comes to fMRI, PET, and similar technologies, we've got you covered. Decision trees and random forest algorithms from machine learning, along with neural networks and RNNs from deep learning, were used to attain the desired accuracy.