# Early Recognition of Parkinson's Disease Through Acoustic Analysis and Machine Learning

arXiv: https://arxiv.org/abs/2407.16091 · HTML: https://mlsanity.com/paper/2407.16091/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization](https://mlsanity.com/paper/1905.04970/) (2019)
- [Efficient Output Kernel Learning for Multiple Tasks](https://mlsanity.com/paper/1511.05706/) (2015)
- [Discriminative Metric Learning with Deep Forest](https://mlsanity.com/paper/1705.09620/) (2017)
- [PRIL: Perceptron Ranking Using Interval Labeled Data](https://mlsanity.com/paper/1802.03873/) (2018)
- [Additive Logistic Models as Interpretable Likelihood-Ratio Scores for AUC-Based Classification](https://mlsanity.com/paper/1511.04803/) (2015)
- [Reliable Prediction Intervals for Local Linear Regression](https://mlsanity.com/paper/1603.05587/) (2016)
- [Structured low-rank matrix learning: algorithms and applications](https://mlsanity.com/paper/1704.07352/) (2017)
- [Cadre Modeling: Simultaneously Discovering Subpopulations and Predictive Models](https://mlsanity.com/paper/1802.02500/) (2018)
- [On the Interaction Effects Between Prediction and Clustering](https://mlsanity.com/paper/1807.06713/) (2018)

## Built on by (most cited first, 1 of 1)

- [A data augmentation strategy for deep neural networks with application to epidemic modelling](https://mlsanity.com/paper/2502.21033/) (2025)

## Abstract (arXiv)

Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly impacts both motor and non-motor functions, including speech. Early and accurate recognition of PD through speech analysis can greatly enhance patient outcomes by enabling timely intervention. This paper provides a comprehensive review of methods for PD recognition using speech data, highlighting advances in machine learning and data-driven approaches. We discuss the process of data wrangling, including data collection, cleaning, transformation, and exploratory data analysis, to prepare the dataset for machine learning applications. Various classification algorithms are explored, including logistic regression, SVM, and neural networks, with and without feature selection. Each method is evaluated based on accuracy, precision, and training time. Our findings indicate that specific acoustic features and advanced machine-learning techniques can effectively differentiate between individuals with PD and healthy controls. The study concludes with a comparison of the different models, identifying the most effective approaches for PD recognition, and suggesting potential directions for future research.