  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,
analyzing which a clinician assesses the severity of the illness. The present
study is an attempt to propose a complementary medical front by mathematically
modeling the "Clinical Eye" of a VIRtual DOCtor, using Statistical and Machine
Intelligence tools (SMI), to analyze 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 analyze other epidemic infections, such as COVID-19.
