Abstract of a Quantitative Research Article

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Abstract of Quantitative Research Article Sushant Anil Patil Department of Information Technology DSRT-837 M22 Professional Writing Dr. Amanda Tanner February 10, 2024

Swain, S., Bhushan, B., Dhiman, G., & Viriyasitavat, W. (2022). Appositeness of optimized and reliable machine learning for healthcare: a survey. Archives of Computational Methods in Engineering, 29(6), 3981-4003. https://doi.org/10.1007/s11831-022-09733-8 Author Qualifications Dr. Subhasmita Swain is a Ph.D. holder in Computer Science and Engineering and is currently associated with the Department of Computer Science and Engineering at KIIT, which is recognized as the University of Bhubaneswar in India. Dr. Bhabesh Bhushan holds a Ph.D. in Computer Science and Engineering and is affiliated with the Department of Computer Science and Engineering at the National Institute of Technology, Rourkela, India. Gaurav Dhiman, on the other hand, holds a master’s degree in computer science and is affiliated with the Department of Computer Science and Engineering at the National Institute of Technology, Kurukshetra, India. Dr. Wijit Viriyasitavat is a Ph.D. holder in Electrical and Computer Engineering and is affiliated with the Department of Electrical Engineering at King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand. Research Concern The study subject explores the significance and efficiency of optimal and dependable machine learning approaches in healthcare applications. The research addresses the gap in understanding the effectiveness and reliability of machine learning (ML) optimizations in healthcare. It evaluates how AI and ML can enhance healthcare delivery, identify challenges, and propose solutions, contributing significantly to a dissertation focused on AI's impact on healthcare. This work offers a foundation for evaluating AI's practical benefits, risks, and future potential in improving patient care, operational efficiency, and clinical decision-making, serving as a critical reference for discussions on AI's transformative role in healthcare. Another major objective of the research is to evaluate the appropriateness of machine learning algorithms in tackling different issues and demands in the healthcare field. With the growing use of machine learning in healthcare, it is necessary to assess the optimization and reliability of these algorithms to guarantee their suitability and efficacy in real-world healthcare situations. Research Purpose Statement and Research Questions or Hypotheses The study's objective is to perform a thorough survey to examine the suitability of optimized and dependable machine learning methods in the healthcare field. The project aims to evaluate machine learning models' performance, accuracy, and resilience when applied to healthcare datasets. The study endeavors to tackle the following critical inquiries: (1) What are the prevailing patterns and difficulties in using machine learning in healthcare?

(2) What is the comparative accuracy and dependability of various machine learning algorithms when applied to healthcare datasets? (3) What variables impact the optimization and reliability of machine learning models in healthcare applications? (4) What are the possible advantages and constraints of using machine learning in the healthcare sector, and how can these obstacles be tackled to improve the efficiency of machine learning-driven healthcare solutions? Precedent literature The study expands upon the current body of knowledge about machine learning and healthcare, using insights from other studies investigating the use of machine learning algorithms in healthcare environments. Prominent literature in this field comprises scholarly articles, academic papers, and comprehensive studies investigating machine learning models' efficacy, precision, and refinement methods in healthcare sectors like medical diagnosis, patient monitoring, disease prognosis, and treatment strategizing. The study also examines pertinent papers on data preparation approaches, feature selection methods, and model assessment metrics used to improve the dependability and efficacy of machine learning algorithms in healthcare applications (Bharadwaj et al., 2021). Methodology The quantitative study used a survey-based research technique to collect data from healthcare professionals, researchers, and practitioners. The poll was conducted among persons in the healthcare business, namely physicians, nurses, healthcare administrators, and data scientists with expertise in healthcare analytics. A stratified sample method was used to guarantee the inclusion of diverse sectors within the healthcare industry, including hospitals, clinics, research institutes, and healthcare technology enterprises. The data collection methods included distributing online questionnaires to the designated sample group, enabling respondents to provide comments and ideas about the suitability and efficacy of machine learning in the healthcare field. Instrumentation A structured online survey developed to collect quantitative information from the respondents was the main instrument used primarily to gather data for the quantitative research. Several closed-ended questions were included in the questionnaire for the survey. These questions were connected to different elements of the use of machine learning in the healthcare industry, including algorithm performance, data pretreatment procedures, model optimization approaches, and obstacles faced in real-world implementation. Findings The healthcare industry has recently embraced machine learning as a valuable tool for enhancing diagnostic precision, patient results, and operational efficiency. The study has uncovered essential findings, including the most frequently utilized machine learning algorithms in healthcare, the factors that inform algorithm choice, and the obstacles encountered when

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refining and implementing machine learning models in clinical environments. The study applied statistical analysis methods, such as descriptive and inferential statistics, to examine survey feedback and uncover data trends, patterns, and correlations.

References: Swain, S., Bhushan, B., Dhiman, G., & Viriyasitavat, W. (2022). Appositeness of optimized and reliable machine learning for healthcare: a survey. Archives of Computational Methods in Engineering , 29 (6), 3981–4003. https://doi.org/10.1007/s11831-022-09733-8 Bharadwaj, H. K., Agarwal, A., Chamola, V., Lakkaniga, N. R., Hassija, V., Guizani, M., & Sikdar, B. (2021). A review on the role of machine learning in enabling IoT based healthcare applications. IEEE Access , 9 , 38859-38890. https://doi.org/10.1109/ACCESS.2021.3059858

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