A disease forecasting algorithm based on single factor correlation analysis and the JacUOD algorithm

There is a close relationship between the occurrence of a variety of diseases and meteorological factors. However, the typical disease forecasting methods are based on history data and the requirement of initial data is strict. To solve these problems, we proposed a disease forecasting algorithm to adapt to real-time data. The proposed algorithm has two contributions: (1) It uses the single factor correlation analysis methods when selecting meteorological factors that affect disease (2) It introduces a new method to calculate disease prediction to build date _number _meteorological factor matrix and use JacUOD algorithm to evaluate the similarity of meteorological factors between the target dates and past ones. To find out the top-N dates are of the maximum similarity with the target one, therefore, we could forecast the number combining the similarity value and the N date´s patient number. Obviously, the number of patient is obtained by calculating the similarity of different dates´ meteorological factors. Experiments show that the algorithm generates a better accuracy than the traditional algorithms in disease prediction.