Performance Analysis of Time Series Forecasting Using Machine Learning Algorithms for Prediction of Ebola Casualties

• Published in: Applications of Computing and Communication Technologies Vol. 899; pp. 320 - 334
• Main Authors: Pandey, Manish Kumar, Subbiah, Karthikeyan
• Format: Book Chapter
• Language: English
• Published: Singapore Springer 01.01.2018; Springer Singapore
• Series: Communications in Computer and Information Science
• Subjects: Ebola; Epidemic Forecasting; Random tree; SMAC; Time series forecasting
• ISBN: 9811320349; 9789811320347
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-981-13-2035-4_28

There is an immense concern on our vigilance for controlling the spread of pandemics such as Ebola, Zika, and H1N1 etc. through state of art technology. The dynamics become very complex of epidemics in sweeping population. Efficient descriptive, predictive, preventive and prescriptive analyses on the huge data generated by SMAC are very crucial for valuable arrangement and associated responsive tactics. In this paper, we have proposed the use of machine learning techniques for performance evaluation of time series forecasting of Ebola casualties. By experimenting without lag creation, we achieved the best results in the MAE of 7.85%, RMSE value of 61.14%, and Direction Accuracy of 85.99% with Random Tree Classifier. Thus we can conclude that by using these models for forecasting epidemic spread and developing public health policies leads the health authorities to ensure the appropriate actions for the control of the outbreak.