An IoT-based Water-borne Diseases Prediction System

Introduction: Internet of Things sensors have facilitated automation in various application areas. Utilization of water-quality sensors can be helpful for water quality monitoring and assessment. Objective: This research study aims to perform the water related disease prediction. Method: The proposed system utilizes novel data augmentation method to address the imbalanced dataset problem, optimal prediction based on voting principle using six classifiers to obtain the efficient model. The proposed system is tested for real time water quality parameters using IoT module. Results and Discussion: For the gradient boosting classifier, the proposed model achieved a higher accuracy of 98.13 percent than the previous system of 95 percent. Conclusion: The proposed disease prediction outperformed the prior disease prediction system with respect to accuracy and execution time.