Prediction of Patients' Illness Based on Average Temperature and Rainfall In Az-Zainiyah Clinic Using Backpropagation Method

Abstract

The purpose of this study is to predict the type of disease in students based on the average temperature and rainfall at the Az-Zainiyah clinic using the backpropagation method. With this research, we hope that the prediction/estimation process on the type of patient's disease using the backpropagation artificial neural network method provides a solution with precise and accurate prediction results based on data on many patients, average temperature, and previous rainfall. This research is carried out through a process of predicting the type of disease by collecting time-series data from patient visit reports. The raw data obtained are examined for completeness and quality of the data. Then, the data was analyzed and applied to an artificial neural network method to predict the type of patient's disease based on many patients, average temperature, and rainfall. Furthermore, the artificial neural network will be optimized by the backpropagation algorithm. From this study, we find that the percentage of precision obtained in the experimental type of pharyngitis/sore throat disease in November 2020 with an average precision percentage is 68.92%, the best precision percentage is 90.25%, and the worst precision percentage 47.59%. In December 2020, the average precision percentage is 41.46%, with the best precision percentage being 65.55%, and the worst precision percentage 4.92%. In the type of dermatitis/itching disease in November 2020, the best precision percentage is 98.81%, the average precision percentage is 68.31%, and the worst precision percentage is -21.57 %. For December 2020, the average precision percentage is -63.27%, the best precision percentage is 48.37%, and the worst precision percentage is -183.85%.