Ground Water Arsenic and Cancer Risk Assessment Prediction Model via Machine Learning: A step towards Modernizing Academic Research

Ground water contamination with Arsenic (As), is one of the foremost issues in the South Asian countries, where ground water is one of the major 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 understand 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 which includes; Support Vector Machine (SVM), Linear Regression (LR), Bayesian Linear Regression (BLR), Boosted Decision Tree (BDT). Exponential Smoothing (ETS) and Autoregressive Integrated Moving Average (ARIMA) methods are used for arsenic forecasting for the next five-years. The developed predictive model named as Arsenic Contamination and Cancer Risk Assessment Prediction (ACCRAP) model will assist for prediction of the arsenic contamination levels and the cancer rate. The obtained results demonstrated that BLR pose highest prediction accuracy of cancer rate among the four deployed machine learning algorithms.