Voice Biomarkers for Parkinson's Disease Prediction Using Machine Learning Models with Improved Feature Reduction Techniques
Journal Title: Journal of Data Science and Intelligent Systems - Year 2023, Vol 1, Issue 2
Abstract
As a chronic and life-threatening disease, Parkinson’s disease (PD) causes people to become rigid and inactive and have shaky voices. There is an argument that current PD detection techniques are ineffective due to their high latency and low accuracy. To enhance the accuracy of PD identification, voice recordings were used as biomarkers in conjunction with the synthetic minority oversampling technique (SMOTE). Three machine learning (ML) models namely support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF) were adopted to calculate the prediction accuracy. By applying an unsupervised dimensional reduction method, the generated model eliminates redundant data and speeds up training and testing. Model performance is estimated with three parameters, including accuracy, F1 score, and area under the curve (AUC) values. Experimental outcomes suggested that the RF model outperforms other models with 97.4% of classification accuracy. This type of research aims to analyze patient voice recordings to determine the disease severity.
Authors and Affiliations
Nalini Chintalapudi, Venkata Rao Dhulipalla, Gopi Battineni, Ciro Rucco, Francesco Amenta
Keywords
Related Articles
- EP ID EP724853
- DOI 10.47852/bonviewJDSIS3202831
- Views 79
- Downloads 0
ARM for Analyzing Factors Influencing Vaccinations During the COVID-19 Outbreak
This article investigates factors influencing Coronavirus 2019 (COVID-19) vaccinations and public concerns using association rule mining (ARM). The experiment was conducted in Phuket at the beginning of 2022 when many pe...
Chemical Engineering Numerical Analysis with R: Peng-Robinson Equation of State
Likely, many text on MATLAB, C++, FORTRAN and Python programming languages exist in chemical engineering libraries, discussing their applications for chemical engineering numerical analysis. R programming language, which...
Symmetric Kernel-Based Approach for Elliptic Partial Differential Equation
In this work, two globally supported and positive definite radial kernels: generalized inverse multiquadric and linear Laguerre Gaussian radial kernels were used to construct symmetric kernel-based interpolating scheme u...
Applications of Quantum Computing in Health Sector
The purpose of this paper is to provide an overview of the current state of quantum computing in the health sector and to explore its potential future applications. Quantum computing has the potential to revolutionize a...
Federated-Based Deep Reinforcement Learning (Fed-DRL) for Energy Management in a Distributive Wireless Network
Studies on developing future generation wireless systems are expected to support increased infrastructure development and device subscriptions with densely deployed base stations (BSs). Economically, decreasing BS energy...