Dengue fever (DF) is a mosquito-borne communicable disease, mostly manifest during South-East Asian monsoon. Approximately 2-5% people die due to severe hemorrhagic Dengue fever and, if left untreated, the number rises up to 20%. Although it is self-limiting, some cases, referred here as Doubtful Cases’ (DfC), may abruptly turn ‘fatal’ often causing mortality in the affected population. Diagnostically apprehending such ‘fatal’ cases is a key medical challenge as clinically it is nearly impossible to predict those. This article outlines a predictive model of dengue infection through a Deep Learning (DL) approach leading to deliver a predictive tool that can accurately preempt clinical ‘morbidity’ patterns. This tool is developed and validated on a set of prototype synthetic Dengue data and ratified against independent opinions from (10) clinicians to predict and grade the infection severity of such DfC case to ca 99% accuracy. The DL approach can be generically extended to other epidemic forms with DfCs that further extends the remit of this study.

A hybrid ML model is created for cardiac risk scoring using GMM clustering to group high-risk populations, Spearman correlation test, PCA, and Chi-square test are used for feature engineering. Significant feature sets are the input set to a feed-forward neural network (M:1;1) for cardiac risk scoring. The data used in this work is synthetic.