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Project-based learning to enrich the impact of machine learning algorithms for medical data analysis in engineering education

Yogita Dubey & Prachi Palsodkar

International Journal of Knowledge and Learning2025https://doi.org/10.1504/ijkl.2025.145988article
AJG 1ABDC C
Weight
0.44

What the paper says

Many higher education institutions (HEI) are implementing project-based learning (PBL) as innovative pedagogy in their curriculum for effective teaching learning. PBL engages students with various phases such as identifying a problem statement, providing a solution to that problem, designing or implementation of that solution with best accuracy. This paper presents the effective methodology for teaching learning process in higher education using PBL to study the impact of machine learning (ML) algorithms for the analysis of medical data for diseases classification. The methodology is supported by hands on workshop conducted for a final year engineering students with feedback and impact analysis, followed by project implementation for five case studies on medical data. To assess the impact of PBL, report submission on these case studies was carried out with rubrics and assessment tools.

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https://doi.org/https://doi.org/10.1504/ijkl.2025.145988

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@article{yogita2025,
  title        = {{Project-based learning to enrich the impact of machine learning algorithms for medical data analysis in engineering education}},
  author       = {Yogita Dubey & Prachi Palsodkar},
  journal      = {International Journal of Knowledge and Learning},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijkl.2025.145988},
}

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.