Doctor unpredicted prescription handwriting prediction using triboelectric smart recognition

Manivannan, P.; Agarwal, Nidhi; Pradhan, Rahul; Muthu, Bala Anand; Kamruzzaman, M. M.; Victor, Akila; Mervin, R.

doi:10.1080/09537287.2023.2202173

Cited by 3 publications

(2 citation statements)

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“…The practical application of the SVM model to real-world voice data transcends theoretical constructs, materializing as a tangible demonstration of predictive potency. Enshrined within the code snippets, the adeptness of the SVM model in navigating both the training and test datasets offers empirical validation of its practical efficacy [24,26]. Moreover, the symphony orchestrated by melding biomedical insights, machine learning capabilities, and the accessibility of datasets resonates with a harmonious rhythm.…”

Section: Related Work and Comparisonmentioning

confidence: 99%

“…•Unveiling Voice-Based Biomarkers: The first objective is to delve into the rich dataset of voice recordings, meticulously examining attributes such as fundamental frequency variations, amplitude nuances, and nonlinear complexity measures. By dissecting these attributes, the aim is to uncover subtle yet distinctive vocal markers that could serve as diagnostic indicators of PD [25][26][27]. This involves understanding how the voice transforms as PD progresses, potentially offering early signs of the condition before overt motor symptoms manifest.…”

Section: Objectivesmentioning

confidence: 99%

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

Dahiya,

Deepakshi

et al. 2024

EAI Endorsed Trans Perv Health Tech

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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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Section: Related Work and Comparisonmentioning

confidence: 99%

Section: Objectivesmentioning

confidence: 99%