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Ground water contamination with Arsenic (As) is one of the foremost issues in the South Asian countries where ground water is one of the foremost sources of drinking water. In Asian countries, especially people of Pakistan living in rural areas are devouring ground water for drinking purpose, and cleaned water is not accessible to them. This arsenic contaminated water is hazardous for human health. The persistence of this study is to study the increasing level of arsenic in ground water in coming years for Khairpur, Sindh Pakistan, which is also increasing the cancer rate (skin cancer, blood cancer) gradually in human body. To predict the arsenic value and cancer risk for the next five years, we have developed two models via Microsoft Azure machine learning with algorithms include Support Vector Machine (SVM), Linear Regression (LR), Bayesian Linear Regression (BLR), Boosted Decision tree (BDT), exponential smoothing ETS, Autoregressive Integrated Moving Average (ARIMA). The developed predictive model named as Arsenic Contamination and Cancer Risk Assessment Prediction Model (ACCRAP model) will help us to forecast the arsenic contamination levels and the cancer rate. The results demonstrated that BLR pose highest prediction accuracy of cancer rate among the four deployed machine learning algorithms.

Background: Diabetes, being the most prevalent co-morbidity associated with various metabolic complaints and is considered as the principal emerging health issue of 21st century. As along with conformist complications, the disease due to having immuno-deficiency characteristics that leads to low functioning of Tcells and neutrophils consequently becomes highly accountable for frequent infections. The present study was conducted to analyze the co-morbidity of infections with diabetes and to describe and control the foremost complications of infectious diseases and their management allied with this metabolic disorder. Methodology: A cross sectional prospective study was conducted in the diabetic clinics of public and private sector hospitals of Karachi. 170 diabetic patients were included in the study, 26 were tested to be pre-diabetic and while the remaining 144 were not only diabetic also on antibiotics. The data was analyzed using the SPSS version 17. Results: The data of study reveals that the frequency of infection was found utmost in diabetic patients, as the antibiotics prescription rate and antiseptic utilization for treating various infections was increased among these cases. As among 144 diabetic patients 50% were found to have frequent infection as were often prescribe antibiotics. Conclusion: We concluded, severe hyperglycemia can drastically alter body immune response against pathogens that strongly favors the deprived outcomes and severe complications. Health care professionals are required to encourage the patients to follow the recommendations and guidelines to reduce the morbidity of diabetes along with infection and prescribe drugs of choice for the management of pathogenic bacteria at early phase to avoid the severity of pathogenicity and recommendation of supplements and medicine that enhances the immunity must be enforced.

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