Version 1
: Received: 17 August 2023 / Approved: 17 August 2023 / Online: 21 August 2023 (02:42:09 CEST)
How to cite:
Adegbite, G. E_Apriori: An Efficient Machine Learning Algorithm for the Control of Malaria. Preprints2023, 2023081388. https://doi.org/10.20944/preprints202308.1388.v1
Adegbite, G. E_Apriori: An Efficient Machine Learning Algorithm for the Control of Malaria. Preprints 2023, 2023081388. https://doi.org/10.20944/preprints202308.1388.v1
Adegbite, G. E_Apriori: An Efficient Machine Learning Algorithm for the Control of Malaria. Preprints2023, 2023081388. https://doi.org/10.20944/preprints202308.1388.v1
APA Style
Adegbite, G. (2023). E_Apriori: An Efficient Machine Learning Algorithm for the Control of Malaria. Preprints. https://doi.org/10.20944/preprints202308.1388.v1
Chicago/Turabian Style
Adegbite, G. 2023 "E_Apriori: An Efficient Machine Learning Algorithm for the Control of Malaria" Preprints. https://doi.org/10.20944/preprints202308.1388.v1
Abstract
The discovery of interesting inter-relationships between the different malaria epidemiological parameters is essential towards the disease control. However, existing associative rule-based machine learning algorithms for pattern discovery are slow while working on high-dimensional Malaria Indicator Survey (MIS) data, with the further challenge of data under fitting and inadequate result visualization. Hence, this work proposed a novel and efficient associative rule-based machine-learning algorithm with enhanced graphical visualization capacity for rigorous and confident biological result interpretation for malaria control. Through empirical and asymptotic comparative time-complexity performance evaluations, the proposed algorithm scaled better than other existing associative rule-based machine learning algorithms while maintaining its accuracy. The algorithm was applied to two real MIS data sets obtained from the Demographic and Health Survey repository and other supplementary literature source using Nigeria as a case study. The resulting interesting malaria epidemiological discovered novel trends were: a) the malaria disease might not be associated with the anemia symptom; b) there was no significant association between the anemia symptom and the wealth indices of individuals; c) there were other parameters associated with the insecticide resistance capacity of the malaria vector asides the knock down resistance alleles; d) the population dynamics of the malaria vector was not associated with the malaria disease endemicity. In conclusion, this work developed a computationally efficient and user-friendly associative rule-based machine-learning algorithm called E_Apriori for the control of the malaria disease.
Keywords
Malaria; Machine Learning; Demographic and Health Survey; biological insights; control interventions
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
