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Background Dengue fever can progress to dengue hemorrhagic fever (DHF), a more serious and occasionally fatal form of the disease. Indicators of serious disease arise about the time the fever begins to reduce (typically 3 to 7 days following symptom onset). There are currently no effective antivirals available. Drug repurposing is an emerging drug discovery process for rapidly developing effective DHF therapies. Through network pharmacology modeling, several US Food and Drug Administration (FDA)-approved medications have already been researched for various viral outbreaks. Objective We aimed to identify potentially repurposable drugs for DHF among existing FDA-approved drugs for viral attacks, symptoms of viral fevers, and DHF. Methods Using target identification databases (GeneCards and DrugBank), we identified human–DHF virus interacting genes and drug targets against these genes. We determined hub genes and potential drugs with a network-based analysis. We performed functional enrichment and network analyses to identify pathways, protein-protein interactions, tissues where the gene expression was high, and disease-gene associations. Results Analyzing virus-host interactions and therapeutic targets in the human genome network revealed 45 repurposable medicines. Hub network analysis of host-virus-drug associations suggested that aspirin, captopril, and rilonacept might efficiently treat DHF. Gene enrichment analysis supported these findings. According to a Mayo Clinic report, using aspirin in the treatment of dengue fever may increase the risk of bleeding complications, but several studies from around the world suggest that thrombosis is associated with DHF. The human interactome contains the genes prostaglandin-endoperoxide synthase 2 (PTGS2), angiotensin converting enzyme (ACE), and coagulation factor II, thrombin (F2), which have been documented to have a role in the pathogenesis of disease progression in DHF, and our analysis of most of the drugs targeting these genes showed that the hub gene module (human-virus-drug) was highly enriched in tissues associated with the immune system (P=7.29 × 10–24) and human umbilical vein endothelial cells (P=1.83 × 10–20); this group of tissues acts as an anticoagulant barrier between the vessel walls and blood. Kegg analysis showed an association with genes linked to cancer (P=1.13 × 10–14) and the advanced glycation end products–receptor for advanced glycation end products signaling pathway in diabetic complications (P=3.52 × 10–14), which indicates that DHF patients with diabetes and cancer are at risk of higher pathogenicity. Thus, gene-targeting medications may play a significant part in limiting or worsening the condition of DHF patients. Conclusions Aspirin is not usually prescribed for dengue fever because of bleeding complications, but it has been reported that using aspirin in lower doses is beneficial in the management of diseases with thrombosis. Drug repurposing is an emerging field in which clinical validation and dosage identification are required before the drug is prescribed. Further retrospective and collaborative international trials are essential for understanding the pathogenesis of this condition.

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Diabetes Mellitus disease prediction is a growing research in healthcare. More over number of data mining methods have been applied to evaluate the main causes of diabetes, but only small sets of clinical risk factors are considered. So the results generated by such methods may not represent exact diabetes. We have to analyse number of factors such as Hereditary and genetics factors, Stress, Body Mass Index, Increased cholesterol level, High carbohydrate diet, Nutritional deficiency, Nature of Exercises, Tension and worries, High blood pressure, Insulin deficiency, Insulin resistance. Then we evaluate and compare this system using suitable rules and Map Reduce algorithm. The performance of the system is assessed in terms of different parameter like rules used, classification accuracy, and classification error. By considering all these parameters, the system can predict diabetics in a great accuracy. Also this paper surveys about different techniques and tools available in Big Data to predict Diabetes mellitus. Big Data can significantly diabetes research and ultimately improves the quality of health care for diabetics patients.