Published Online:January 2025
Product Name:The IUP Journal of Computer Sciences
Product Type:Article
Product Code:IJCS030125
DOI:10.71329/IUPJCS/2025.19.1.35-59
Author Name:Iqra Jan and Shabir Sofi
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:35-59
The paper analyzes the performance of various lightweight basic and boosting ML classification algorithms by taking the default values of their parameters, and considering the optimized use of resources like processing unit, memory utilization, etc. Related papers from 2019 to 2023 from various online databases, like IEEE Xplore, Web of Science, Google Scholar and PubMed, were extracted to examine the relevant work on medical data classification. The paper presents boosting ML classification algorithms and compares their performance with that of basic ML classification algorithms on five benchmark datasets. Resource utilization has also been analyzed for both basic and boosting lightweight ML algorithms. It was found that boosting ML algorithms outperformed the basic ML algorithms on each dataset.
Medical data is defined as health-related information associated with routine patient care that is gathered and saved anytime a patient receives medical attention or is continually monitored, either by being admitted to the hospital or using smart monitoring equipment.