Voice-Based Classification of Patients Using Various Machine Learning & Deep Learning Techniques in Relation with Business Perspectives


  • Satyajit Pattnaik


This report presents a unique research proposal that focuses on exploring the commercial implications of voice-based patient categorization through the utilization of machine learning and deep learning algorithms. The study encompasses a comprehensive approach, consisting of an extensive literature review, the development of modeling techniques, and the analysis of case studies to accomplish its research objectives.
The initial literature review conducted sheds light on the effectiveness of deep learning and machine learning methodologies, such as 1D Convolutional Neural Networks (CNNs) and Fourier transformation, in effectively distinguishing between normal and diseased voices. Moreover, the review showcases various studies that have examined the
business perspectives of voice-based classification, showcasing its potential applications in domains such as customer service and market research.
The research strategy outlined in the discussion section emphasizes the significance of conducting a comprehensive literature review to gain insights into the existing knowledge in the field of voice-based classification. In addition, the study proposes the utilization of modeling techniques to enhance and develop deep learning and machine learning models for voice-based categorization. This includes employing methods such as 1D CNNs, Fourier transformation, and 2D CNNs with spectrograms. Furthermore, a detailed case study analysis will be conducted to explore the practical implementation and commercial applications of voice-based categorization across diverse business contexts.
By addressing the commercial aspects of voice-based classification, this proposed research aims to bridge the existing knowledge gap. It seeks to provide valuable insights into the potential applications of voice-based categorization within various industries while identifying factors such as organizational preparedness, technological requirements, and data protection considerations. The inclusion of case studies will serve to illustrate how voice-based categorization can be seamlessly integrated into existing business processes, highlighting both the advantages and challenges associated with its implementation.
The anticipated outcomes of this research endeavor encompass the identification of novel applications for voice-based categorization, providing guidance for businesses contemplating its implementation, and fostering a deeper understanding of the benefits and limitations of this technology. Additionally, by establishing connections between the healthcare and business domains, this study aims to stimulate innovation and explore the broader applications of voice-based categorization beyond the medical field.




How to Cite

Pattnaik, S. . (2024). Voice-Based Classification of Patients Using Various Machine Learning & Deep Learning Techniques in Relation with Business Perspectives. Global Journal of Business and Integral Security. Retrieved from https://gbis.ch/index.php/gbis/article/view/336