Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Ruby Dahiya; Virendra Kumar Dahiya; Deepakshi; Nidhi Agarwal; Lakshmana Phaneendra Maguluri; Elangovan Muniyandy

doi:10.4108/eetpht.10.5519

Authors

Ruby Dahiya Galgotias University
Virendra Kumar Dahiya Galgotias University
Deepakshi Indira Gandhi Delhi Technical University for Women
Nidhi Agarwal Indira Gandhi Delhi Technical University for Women
Lakshmana Phaneendra Maguluri Koneru Lakshmaiah Education Foundation
Elangovan Muniyandy Saveetha Institute of Medical and Technical Sciences

DOI:

https://doi.org/10.4108/eetpht.10.5519

Keywords:

Parkinson's Disease, Biomedical Voice Measurements, Machine Learning, Early Diagnosis, Support Vector Machine, SVM

Abstract

INTRODUCTION: Parkinson's Disease (PD), a progressively debilitating neurological disorder impacting a substantial global population, stands as a significant challenge in modern healthcare. The gradual onset of motor and non-motor symptoms underscores the criticality of early detection for optimal treatment outcomes. In response to this urgency, novel avenues for early diagnosis are being explored, where the amalgamation of biomedical voice analysis and advanced machine learning techniques holds immense promise. Individuals afflicted by PD experience a nuanced deterioration of bodily functions, necessitating interventions that are most effective when initiated at an early stage. The potential of biomedical voice measurements to encode subtle health indicators presents an enticing opportunity. The human voice, an intricate interplay of frequencies and patterns, might offer insights into the underlying health condition.

OBJECTIVES: This research embarks on a comprehensive journey to delve into the intricate connections between voice attributes and the presence of PD, with the aim of expediting its detection and treatment.

METHODS: At the heart of this exploration is the Support Vector Machine (SVM) model, a versatile machine learning tool [1-2]. Functioning as a virtual detective, the SVM model learns from historical data to decipher the intricate patterns that differentiate healthy individuals from those with PD [3-4].

RESULTS: Through the power of pattern recognition, the SVM becomes a predictive instrument, a potential catalyst in unravelling the latent manifestations of PD using the unique patterns harbored within the human voice. Embedded within this research are the practical demonstrations showcased through code snippets [5-7]. By synergizing the intricate voice measurements with the SVM model, we envision the emergence of a diagnostic paradigm where early PD detection becomes both accessible and efficient. This study not only epitomizes the synergy of voice and machine interactions but also attests to the transformative potential of technology within the domain of healthcare. .

CONCLUSION: Ultimately, this research strives to harness the intricate layers of voice data, as exemplified through the provided model code [8-11], to contribute to the evolution of an advanced tool for PD prediction. By amalgamating the principles of machine learning and biomedical analysis, we aspire to expedite early PD diagnosis, thereby catalyzing more efficacious treatment strategies. In traversing this multidimensional exploration, we aspire to pave the path toward a future where technology plays an instrumental role in enhancing healthcare outcomes for individuals navigating the challenges of PD, ultimately advancing the pursuit of early diagnosis and intervention.

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