The IUP Journal of Information Technology
Time Series Forecasting Models for Predicting Conjunctivitis Disease Cases

Article Details
Pub. Date : Dec, 2019
Product Name : The IUP Journal of Information Technology
Product Type : Article
Product Code : IJIT31912
Author Name : Shobhit Verma, Nonita Sharma
Availability : YES
Subject/Domain : Engineering
Download Format : PDF Format
No. of Pages : 11



In recent times, machine learning is a powerful technique for data analysis and for making future prediction. There are many existing forecasting models that are useful in predicting different areas. Acute conjunctivitis, commonly known as "pink eye", is one of the most common eye infections, particularly among school children. Because of its highly contagious nature, everyone is susceptible, especially those in crowded places such as kindergarten, indoor amusement parks and swimming pools. Hence as a precautionary measure, there is an imperative need to predict the future possibilities of conjunctivitis cases. The paper uses machine learning-based forecasting models for predicting conjunctivitis cases in Hong Kong. The analysis is conducted on the data of past years conjunctivitis cases in Hong Kong. The mean forecast, seasonal naive, auto ARIMA and neural network techniques are used for analysis and forecasting. The surpassing model is adopted based on the accuracy factor. The accuracy of the models is compared with respect to root mean square error and auto correlation function. The results reveal that the neural network model produces the least error and hence is the best prediction model for the dataset in terms of accuracy.


In today's life, machine learning models are used too much for prediction and decision making in different areas such as stock market, medical field, banking and sales forecasting. There are many diseases which are of concern for human health like heart disease, alzheimer, tuberculosis, conjunctivitis, dengue and many more. In Hong Kong, each week many cases of conjunctivitis are occurring. The health organization is taking many initiatives to put a stop to the occurrence of different diseases. But we cannot do anything until we have pre-information about the disease occurrence and the number of cases of the particular disease. Therefore, there is a need to predict future occurrences so that the total number of cases that might occur in the near future can be predicted in advance and necessary action can be taken to curb that.


Conjunctivitis, Time series, Forecasting, Seasonal naive, Neural networks, Mean forecast

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