VIRDOCD:a VIRtual DOCtor to Predict Dengue Fatality

Abstract

Clinicians make routine diagnosis by scrutinizing patients' medical signs and symptoms, a skill popularly referred to as ‘Clinical Eye’. This skill evolves through trial-and-error and improves with time. The success of the therapeutic regime relies largely on the accuracy of interpretation of such sign-symptoms, analysing which a clinician assesses the severity of the illness. The present study is an attempt to propose a complementary medical front by mathematically modelling the ‘Clinical Eye’ of a VIRtual DOCtor, using statistical and machine intelligence tools (SMI), to analyse Dengue epidemic infected patients (100 case studies with 11 weighted sign-symptoms). The SMI in VIRDOCD reads medical data and translates these into a vector comprising multiple linear regression (MLR) coefficients to predict infection severity grades of dengue patients that clone the clinician's experience-based assessment. Risk managed through ANOVA, the dengue severity grade prediction accuracy from VIRDOCD is found higher (ca 75%) than conventional clinical practice (ca 71.4%, mean accuracy profile assessed by a team of 10 senior consultants). Free of human errors and capable of deciphering even minute differences from almost identical symptoms (to the Clinical eye), VIRDOCD is uniquely individualized in its decision-making ability. The algorithm has been validated against Random Forest classification (RF, ca 63%), another regression-based classifier similar to MLR that can be trained through supervised learning. We find that MLR-based VIRDOCD is superior to RF in predicting the grade of Dengue morbidity. VIRDOCD can be further extended to analyse other epidemic infections, such as COVID-19.

Publication DOI: https://doi.org/10.1111/exsy.12796
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: This is the peer reviewed version of the following article: Chattopadhyay, A. K., & Chattopadhyay, S. (2021). VIRDOCD: A VIRtual DOCtor to predict dengue fatality. Expert Systems, e12796, which has been published in final form at https://doi.org/10.1111/exsy.12796.  This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Uncontrolled Keywords: ANOVA,Random Forest,case fatality,dengue,multiple linear regressions,predictive modelling,python,statistical modelling,Control and Systems Engineering,Theoretical Computer Science,Computational Theory and Mathematics,Artificial Intelligence
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Related URLs: https://onlinel ... 1111/exsy.12796 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-09-13
Published Online Date: 2021-09-13
Accepted Date: 2021-08-06
Authors: Chattopadhyay, Amit (ORCID Profile 0000-0001-5499-6008)
Chattopadhyay, Subhagata

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 13 September 2022.


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