Evaluating Echo State Network for Parkinson's Disease Prediction using Voice Features

• URL: http://arxiv.org/abs/2401.15672v1
• Date: Sun, 28 Jan 2024 14:39:43 GMT
• Title: Evaluating Echo State Network for Parkinson's Disease Prediction using Voice Features
• Authors: Seyedeh Zahra Seyedi Hosseininian, Ahmadreza Tajari, Mohsen Ghalehnoie, Alireza Alfi
• Abstract summary: This study aims to develop a diagnostic model capable of achieving both high accuracy and minimizing false negatives. Various machine learning methods, including Echo State Networks (ESN), Random Forest, k-nearest Neighbors, Support Vector, Extreme Gradient Boosting, and Decision Tree, are employed and thoroughly evaluated. ESN consistently maintains a false negative rate of less than 8% in 83% of cases.
• Score: 1.2289361708127877
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Parkinson's disease (PD) is a debilitating neurological disorder that necessitates precise and early diagnosis for effective patient care. This study aims to develop a diagnostic model capable of achieving both high accuracy and minimizing false negatives, a critical factor in clinical practice. Given the limited training data, a feature selection strategy utilizing ANOVA is employed to identify the most informative features. Subsequently, various machine learning methods, including Echo State Networks (ESN), Random Forest, k-nearest Neighbors, Support Vector Classifier, Extreme Gradient Boosting, and Decision Tree, are employed and thoroughly evaluated. The statistical analyses of the results highlight ESN's exceptional performance, showcasing not only superior accuracy but also the lowest false negative rate among all methods. Consistently, statistical data indicates that the ESN method consistently maintains a false negative rate of less than 8% in 83% of cases. ESN's capacity to strike a delicate balance between diagnostic precision and minimizing misclassifications positions it as an exemplary choice for PD diagnosis, especially in scenarios characterized by limited data. This research marks a significant step towards more efficient and reliable PD diagnosis, with potential implications for enhanced patient outcomes and healthcare dynamics.

Related papers

• MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study [0.7751705157998379]
  Alzheimer's disease (AD) and vascular dementia (VaD) are the two most prevalent dementia types. This paper presents an innovative multi-omics approach to accurately differentiate AD from VaD, achieving a diagnostic accuracy of 89.25%.
  arXiv  Detail & Related papers  (2024-11-06T10:13:28Z)
• Improving Machine Learning Based Sepsis Diagnosis Using Heart Rate Variability [0.0]
  This study aims to use heart rate variability (HRV) features to develop an effective predictive model for sepsis detection. A neural network model is trained on the HRV features, achieving an F1 score of 0.805, a precision of 0.851, and a recall of 0.763.
  arXiv  Detail & Related papers  (2024-08-01T01:47:29Z)
• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A feasibility study [12.4123972735841]
  Primary Immune thrombocytopenia (ITP) is a rare autoimmune disease characterised by immune-mediated destruction of peripheral blood platelets in patients. There is no established test to confirm the disease and no biomarker with which one can predict the response to treatment and outcome. We conduct a feasibility study to check if machine learning can be applied effectively for diagnosis of ITP using routine blood tests and demographic data in a non-acute outpatient setting.
  arXiv  Detail & Related papers  (2024-05-31T01:04:46Z)
• Unified Uncertainty Estimation for Cognitive Diagnosis Models [70.46998436898205]
  We propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. We decompose the uncertainty of diagnostic parameters into data aspect and model aspect. Our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis.
  arXiv  Detail & Related papers  (2024-03-09T13:48:20Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• ADiag: Graph Neural Network Based Diagnosis of Alzheimer's Disease [0.0]
  Alzheimer's Disease (AD) is the most widespread neurodegenerative disease, affecting over 50 million people across the world. Currently, only qualitative means of testing are employed in the form of scoring performance on a battery of cognitive tests. We have developed ADiag, a novel quantitative method to diagnose AD through GraphSAGE Network and Dense Differentiable Pooling (DDP) analysis. Preliminary tests of ADiag have revealed a robust accuracy of 83%, vastly outperforming other qualitative and quantitative diagnostic techniques.
  arXiv  Detail & Related papers  (2021-01-08T06:23:30Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Machine learning discrimination of Parkinson's Disease stages from walker-mounted sensors data [0.0]
  This study applies machine learning methods to discriminate six stages of Parkinson's Disease (PD) progression. The data was acquired by low cost walker-mounted sensors in an experiment at a movement disorders clinic.
  arXiv  Detail & Related papers  (2020-06-22T09:34:12Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.