METHOD FOR DETECTING PARKINSON'S DISEASE (PD)

Abstract

ABSTRACT A method (100) for detecting Parkinson's disease (PD). Further, the method comprising collecting speech data from individuals, including both diagnosed PD patients and healthy controls. Further, the method (100) comprising the steps of pre-processing the collected speech data to extract relevant features, such as pitch, formant frequency, and Mel-frequency cepstral coefficients (MFCCs). Further, the method (100) comprising the steps of training one or more machine learning algorithms on the extracted features to classify the presence or absence of PD. Further, the method (100) comprising the steps of evaluating the performance of the trained algorithms using metrics such as accuracy, precision, recall, and F1-score on a separate test dataset. Further, the method (100) comprising the steps of deploying the trained machine learning model in a clinical setting for early detection of Parkinson's disease based on speech analysis.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of neurodegenerative disease detection and, in particular, relates to a method for utilizing machine learning algorithms to identify and diagnose Parkinson's disease through the analysis of speech patterns and other relevant data sources.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects movement and coordination. It is characterized by symptoms , Claims:1. A method (100) for detecting Parkinson's disease (PD), the method comprising the steps of:
collecting speech data from individuals, including both diagnosed PD patients and healthy controls;
pre-processing the collected speech data to extract relevant features, such as pitch, formant frequency, and Mel-frequency cepstral coefficients (MFCCs);
training one or more machine learning algorithms on the extracted features to classify the presence or absence of PD;
evaluating the performance of the trained algorithms using metrics such as accuracy, precision, recall, and F1-score on a separate test dataset; and
deploying the trained machine learning model in a clinical setting for early detection of Parkinson's disease based on speech analysis.

2. The method (100) as claimed in claim 1, wherein the machine learning algorithms include decision trees, support vector machines (SVM), and neural networks, and the evaluation step further includes comparing the performance of these algorithms to determine the most eff

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