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. 2024 Sep 27:10:20552076241284376.
doi: 10.1177/20552076241284376. eCollection 2024 Jan-Dec.

Automatic selection model to identify neurodegenerative diseases

Eddy Sánchez-DelaCruz  1 , Cecilia-Irene Loeza-Mejía  1 , César Primero-Huerta  1   2 , Mirta Fuentes-Ramos  1
Affiliations

Automatic selection model to identify neurodegenerative diseases

Eddy Sánchez-DelaCruz et al. Digit Health. .

Abstract

Objective: This study evaluates machine learning algorithms' effectiveness in classifying Parkinson's disease and Huntington's disease based on biomarker data obtained non-invasively from patients and healthy controls.

Methods: Datasets containing biomarker data (x, y, and z values of accelerometers) from sensors were collected from Parkinson's disease, Huntington's disease patients, and healthy controls. An automatic selection model method was implemented for disease classification, using a unique Mexican database of human gait biomarkers, which we consider the only one of its kind. Random forest, random subspace method, and K-star algorithms were employed, with parameters optimized through an automated model selection.

Results: The study achieved a 0.893 precision rate for Parkinson's disease and Huntington's disease using the random subspace method. The findings underscore the potential of machine learning techniques in medical diagnosis, particularly in neurological disorders.

Conclusion: The automatic selection model method demonstrated efficacy in classifying Parkinson's disease and Huntington's disease based on non-invasive biomarker data. This research contributes to advancing non-invasive diagnostic approaches in neurological disorders, highlighting the significance of machine learning in healthcare.

Keywords: Huntington’s disease; Parkinson’s disease; automated machine learning; biomarkers; machine learning.

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Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Pipeline of PD and HD categorization by using machine learning. PD: Parkinson’s disease; HD: Huntington’s disease.
Figure 2.
Figure 2.
Examples of the sensors used to obtain the dataset.
Figure 3.
Figure 3.
Precision and recall graph of Parkinson’s disease (PD) versus control using random forest.
Figure 4.
Figure 4.
Precision and recall graph of PD versus HD using random subspace. PD: Parkinson’s disease; HD: Huntington’s disease.
Figure 5.
Figure 5.
Precision and recall graph for Huntington’s disease (HD) versus control using K-star.
See this image and copyright information in PMC

References

    1. Sousa A, Meyer KA, Santpere G, et al.. Evolution of the human nervous system function, structure and development. Cell 2017; 170: 226–247. - PMC - PubMed
    1. Dugger BN, Dickson DW. Pathology of neurodegenerative diseases. Cold Spring Harb Perspect Biol 2017; 9: a028035. - PMC - PubMed
    1. Ireland D, Wang Z, Lamont R, et al.. Classification of movement of people with Parkinsons disease using wearable inertial movement units and machine learning. In: Digital health innovation for consumers, clinicians, connectivity and community. IOS Press, 2016, pp.61–66. - PubMed
    1. Ross CA, Tabrizi SJ. Huntington’s disease: from molecular pathogenesis to clinical treatment. Lancet Neurol 2011; 10: 83–98. - PubMed
    1. Hayes MT. Parkinson’s disease and parkinsonism. Am J Med 2019; 132: 802–807. - PubMed

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