Recently there has been an increase in the number of diabetic patients at an alarming rate. Approximately 18 million people die from cardiovascular diseases every year where diabetes is one of the major factor. Treating diabetes and monitoring it is required to efficiently manage health conditions of diabetic patients. In this paper, an android application has been designed and developed that recommends probable medication, diet and exercise to help people manage their diabetes well. This system analyses the input parameters that are entered by the end user and provides personalized services for users in the form of recommendations for their diet, medicines and exercises. This android-based system can also remind users to carry out the recommendations, which are provided by the system. Other than the functional features, there are also several important non-functional features of the extensibility and the convenience for use. The recommendation is done using User based collaborative filtering, the system asks the user to enter a predetermined set of parameters which are matched with other patients parameters stored in the database, the database consists of past cases of patients who have been diagnosed with diabetes and treated, this matching is done using Pearson Correlation, the matched patient's diet, exercise and workout is then recommended to the current user.

The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.

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