Artificial Intelligence for Symptomatic Detection and Classification of Suspected Cases of Filovirus Disease in Africa: An Explorative Approach |
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| © 2023 by IJCTT Journal | ||
| Volume-71 Issue-11 |
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| Year of Publication : 2023 | ||
| Authors : Amadi Gloria Yaa, Omotosho Olawale J, Ebiesuwa Oluwaseun, Olujimi Alao, Oludele Awodele, Eze Monday O, Kuyoro Afolashade, Maitanmi Olusola S, Kalesanwo Olamide, Adigun Taiwo O, Owhonda Golden, Ngofa Reuben | ||
| DOI : 10.14445/22312803/IJCTT-V71I11P104 | ||
How to Cite?
Amadi Gloria Yaa, Omotosho Olawale J, Ebiesuwa Oluwaseun, Olujimi Alao, Oludele Awodele, Eze Monday O, Kuyoro Afolashade, Maitanmi Olusola S, Kalesanwo Olamide, Adigun Taiwo O, Owhonda Golden, Ngofa Reuben, "Artificial Intelligence for Symptomatic Detection and Classification of Suspected Cases of Filovirus Disease in Africa: An Explorative Approach," International Journal of Computer Trends and Technology, vol. 71, no. 11, pp. 22-30, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I11P104
Abstract
Misdiagnosis exists as an inevitable human error factor in healthcare operations; diagnostic errors contribute to numerous complications that result in a high death rate. The problems of misdiagnosis and delays in disease identification, especially highly infectious diseases like Filovirus disease, have drastically increased the death rate, especially during the outbreak, according to International Health Organizations and Statistics on Improving Diagnosis in Healthcare. This overwhelming challenge has contributed majorly to many complications that increase casualties, especially during the Filovirus disease outbreak. However, studies conducted in this area have identified some technological drawbacks associated with computational complexities, such as the curse of dimensionality, overfitting, weight and bias, etc., as major challenges in implementing machine learning techniques. Emerging solutions are recommended to better improve healthcare delivery in this domain. This study aimed to develop a model that can be used to provide precise symptomatic detection and classification of suspected cases of filovirus disease with reference to the Ebola Virus and Marburg virus disease. The study obtained the filovirus dataset from multiple sources involving states, Federal and International Health Organizations over time within Africa. The unstructured data collected was preprocessed on Sklearn with Pandas NumPy and split into 8:2 as train and test datasets. Machine Learning Algorithms like Logistic Regression, Linear Discriminant Analysis, Decision Tree, Support Vector Machine, Random Forest, etc were adopted by the Scikit-learn library for Classification analysis. The Confusion Matrix was implemented as a basic ML Statistical analytical framework, and the Accuracy, Precision, Recall, F1-score and a few other metrics were applied for the model Performance Evaluation. The experimental result of the study obtained the highest accuracy of 96% ratio for Logistic Regression, followed by the Linear Discriminant Analysis and Naïve Bayes that offered relatively 95% and 93% rates of Accuracy performance. This outcome suggests that the best-recommended algorithm suitable for the objective of this study is the Logistic Regression model. However, as discovered, the Logistic Regression can be recommended with Feature Selection methods to provide a better dimensional reduction model for future improvement.
Keywords
Artificial Intelligence, Machine Learning Algorithms, Classification Model, Symptomatic Detection of Suspected Cases, Filovirus diseases, Ebola Virus Disease, Marburg Virus Disease.
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