Predicting Case Fatality of Dengue Epidemic: Statistical Machine Learning Towards a Virtual Doctor


Dengue fever is a self-limiting communicable viral disease, transmitted through mosquito bites. Its Case Fatality Grade (CFG) varies across population due to variations in viral load, immunity of the patient, early diagnosis, and availability of high-end treatment facility. This study describes an initial effort to automate the process of Dengue CFG predictions. Two established Statistical Machine Learning (SML) algorithms, Multiple Linear Regressions (MLR) and Multinomial Logistic Regressions (MnLR), are combined to substitute the existing Deep Learning methods for clinical decision making. We consider a vector of eleven sign-symptoms (independent variables), each weighted between [0,1] on a 3-point scale - ‘Mild’ (CFG<=0.33), ‘Moderate’ (0.33<CFG< 0.66), and ‘Severe’ (CFG>0.66). Results show that both classifiers are effective in early screening with similar accuracy levels (68% for MLR versus 72% for MnLR) although precision levels are far superior with MnLR (88%) than MLR (61%). This study is a futuristic step towards Machine Learning (ML) aided clinical diagnostic paradigms, as an alternative to computationally intensive Artificial Intelligence.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 2021 Chattopadhyay et al.; Licensee Savvy Science Publisher. This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited. Funding: The authors gratefully acknowledge partial financial support from the H2020-MSCA-RISE2016 program, grant no. 734485, entitled ‘Fracture Across Scales and Materials, Processes and Disciplines (FRAMED)’.
Uncontrolled Keywords: Statistical machine learning,Dengue epidemic,Multiple linear regressions,Multinomial logistic regressions
Publication ISSN: 2311-8792
Last Modified: 18 Jun 2024 07:40
Date Deposited: 21 Oct 2021 07:42
Full Text Link:
Related URLs: http://savvysci ... loads/jndtv7a2/ (Publisher URL)
PURE Output Type: Article
Published Date: 2021-10-15
Accepted Date: 2021-09-28
Authors: Chattopadhyay, Subhagata
Chattopadhyay, Amit (ORCID Profile 0000-0001-5499-6008)
Aifantis, Elias


Export / Share Citation


Additional statistics for this record