A modular deep learning architecture for interpretable disease prediction across tabular clinical and biometric datasets.
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| Title: | A modular deep learning architecture for interpretable disease prediction across tabular clinical and biometric datasets. |
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| Authors: | Rathod VU; Department of CSE (Artificial Intelligence and Machine Learning), Vishwakarma Institute of Technology (Affiliated to Savitribai Phule Pune University, Pune), Pune, Maharashtra, India., Amrutkar SS; Department of CSE (Artificial Intelligence and Machine Learning), Vishwakarma Institute of Technology (Affiliated to Savitribai Phule Pune University, Pune), Pune, Maharashtra, India., Patil KA; Department of Information Technology, MET's Institute of Engineering (Affiliated to Savitribai Phule Pune University, Pune), Nashik, Maharashtra, India., Londhe AD; Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology (Affiliated to Savitribai Phule Pune University Pune), Pune, Maharashtra, India., Bobade SY; Department of Computer Science and Engineering (Artificial Intelligence), Vishwakarma Institute of Technology (Affiliated to Savitribai Phule Pune University Pune), Pune, Maharashtra, India., Dhotre VA; Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Vishwakarma Institute of Technology (Affiliated to Savitribai Phule Pune University, Pune), Pune, Maharashtra, India., Kebede MW; Department of Information Technology, Institute of Technology, Debre Markos University, Debre Markos, Ethiopia. |
| Source: | PloS one [PLoS One] 2026 May 08; Vol. 21 (5), pp. e0348670. Date of Electronic Publication: 2026 May 08 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0348670 |
