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

被引：0
作者：
Dahiya R. [1 ]
Dahiya V.K. [2 ]
Deepakshi [3 ]
Agarwal N. [3 ]
Maguluri L.P. [4 ]
Muniyandy E. [5 ]
机构：
[1] Department of Computer Science Engineering, Galgotias University, Greater Noida
[2] School of Business, Galgotias University, Greater Noida
[3] Department of CSE, Indira Gandhi Delhi Technical University for Women, Delhi
[4] Dept.of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur Dist., Andhra Pradesh, Vaddeswaram
[5] Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, Chennai
来源：
EAI Endorsed Transactions on Pervasive Health and Technology | 2024年 / 10卷
关键词：
Biomedical Voice Measurements; Early Diagnosis; Machine Learning; Parkinson's Disease; Support Vector Machine (SVM);
D O I：
10.4108/eetpht.10.5519
中图分类号：
学科分类号：
摘要：
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. © 2024 R. Dahiya et al.
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