21,216 research outputs found
A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings
People with Parkinson's disease must be regularly monitored by their
physician to observe how the disease is progressing and potentially adjust
treatment plans to mitigate the symptoms. Monitoring the progression of the
disease through a voice recording captured by the patient at their own home can
make the process faster and less stressful. Using a dataset of voice recordings
of 42 people with early-stage Parkinson's disease over a time span of 6 months,
we applied multiple machine learning techniques to find a correlation between
the voice recording and the patient's motor UPDRS score. We approached this
problem using a multitude of both regression and classification techniques.
Much of this paper is dedicated to mapping the voice data to motor UPDRS scores
using regression techniques in order to obtain a more precise value for unknown
instances. Through this comparative study of variant machine learning methods,
we realized some old machine learning methods like trees outperform cutting
edge deep learning models on numerous tabular datasets.Comment: Accepted at "HIMS'20 - The 6th Int'l Conf on Health Informatics and
Medical Systems"; https://americancse.org/events/csce2020/conferences/hims2
Discriminative Features via Generalized Eigenvectors
Representing examples in a way that is compatible with the underlying
classifier can greatly enhance the performance of a learning system. In this
paper we investigate scalable techniques for inducing discriminative features
by taking advantage of simple second order structure in the data. We focus on
multiclass classification and show that features extracted from the generalized
eigenvectors of the class conditional second moments lead to classifiers with
excellent empirical performance. Moreover, these features have attractive
theoretical properties, such as inducing representations that are invariant to
linear transformations of the input. We evaluate classifiers built from these
features on three different tasks, obtaining state of the art results
Objective dysphonia quantification in vocal fold paralysis: comparing nonlinear with classical measures
Clinical acoustic voice recording analysis is usually performed using classical perturbation measures including jitter, shimmer and noise-to-harmonic ratios. However, restrictive mathematical limitations of these measures prevent analysis for severely dysphonic voices. Previous studies of alternative nonlinear random measures addressed wide varieties of vocal pathologies. Here, we analyze a single vocal pathology cohort, testing the performance of these alternative measures alongside classical measures.

We present voice analysis pre- and post-operatively in unilateral vocal fold paralysis (UVFP) patients and healthy controls, patients undergoing standard medialisation thyroplasty surgery, using jitter, shimmer and noise-to-harmonic ratio (NHR), and nonlinear recurrence period density entropy (RPDE), detrended fluctuation analysis (DFA) and correlation dimension. Systematizing the preparative editing of the recordings, we found that the novel measures were more stable and hence reliable, than the classical measures, on healthy controls.

RPDE and jitter are sensitive to improvements pre- to post-operation. Shimmer, NHR and DFA showed no significant change (p > 0.05). All measures detect statistically significant and clinically important differences between controls and patients, both treated and untreated (p < 0.001, AUC > 0.7). Pre- to post-operation, GRBAS ratings show statistically significant and clinically important improvement in overall dysphonia grade (G) (AUC = 0.946, p < 0.001).

Re-calculating AUCs from other study data, we compare these results in terms of clinical importance. We conclude that, when preparative editing is systematized, nonlinear random measures may be useful UVFP treatment effectiveness monitoring tools, and there may be applications for other forms of dysphonia.

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