VIRDOC: Statistical and Machine Learning by a VIRtual DOCtor to Predict Dengue Fatality

• URL: http://arxiv.org/abs/2104.14282v1
• Date: Thu, 29 Apr 2021 12:06:05 GMT
• Title: VIRDOC: Statistical and Machine Learning by a VIRtual DOCtor to Predict Dengue Fatality
• Authors: Amit K Chattopadhyay and Subhagata Chattopadhyay
• Abstract summary: Clinicians conduct routine diagnosis by scrutinizing signs and symptoms of patients in treating epidemics. The success of the therapeutic regimen relies largely on the accuracy of interpretation of such sign-symptoms, based on which the clinician ranks the potent causes of the epidemic and analyzes their interdependence to devise sustainable containment strategies. This study proposed an alternative medical front, a VIRtual DOCtor (VIRDOC), that can self-consistently rank key contributors of an epidemic and also correctly identify the infection stage, using the language of statistical modelling and Machine Learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinicians conduct routine diagnosis by scrutinizing signs and symptoms of patients in treating epidemics. This skill evolves through trial-and-error and improves with time. The success of the therapeutic regimen relies largely on the accuracy of interpretation of such sign-symptoms, based on which the clinician ranks the potent causes of the epidemic and analyzes their interdependence to devise sustainable containment strategies. This study proposed an alternative medical front, a VIRtual DOCtor (VIRDOC), that can self-consistently rank key contributors of an epidemic and also correctly identify the infection stage, using the language of statistical modelling and Machine Learning. VIRDOC analyzes medical data and then translates these into a vector comprising Multiple Linear Regression (MLR) coefficients to probabilistically predict scores that compare with clinical experience-based assessment. The VIRDOC algorithm, risk managed through ANOVA, has been tested on dengue epidemic data (N=100 with 11 weighted sign-symptoms). Results highly encouraging with ca 75% accurate fatality prediction, compared to 71.4% from traditional diagnosis. The algorithm can be generically extended to analyze other epidemic forms.

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