MEASLES PREDICTION MODEL USING MACHINE LEARNING ALGORITHM (SPECIALIST HOSPITAL DAMATURU, YOBE STALE)
Abstract
Measles prediction using time series analysis Is a radios and repetitive task, which requires a lot of skills. It will be so interesting that a computerized system is developed to alleviate the problems encountered in prediction of measles using time series analysis. In this regard, this project work is taking up to developed a software package that computerizes the prediction of measles using time series analysis with the records of measles of measles at yobe state specialist hospital Damaturu for the period of five years, from 2019 to 2023. Autoregressive Integrated Moving Average (ARIMA) Prediction Model was implemented using c sharp programming language and SQLite for the database for prediction based on collected data; result show consistence in measles for years upward with the corresponding value, 2024:39.33, 2025:84.667,2026:112.333. Therefore the system would be of value to health personal, policy marker in decision making to control measles in a given vicinity.
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