Application of machine learning models to predict malaria using malaria cases and environmental risk factors-Reference-Cited by-同舟云学术

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Application of machine learning models to predict malaria using malaria cases and environmental risk factors

Published:2022-03 Issue: Volume: Page:
ISSN:
Container-title:2022 Conference on Information Communications Technology and Society (ICTAS)
language:
Short-container-title:

Author:

Mbunge Elliot¹,Millham Richard C²,Sibiya Maureen Nokuthula³,Takavarasha Sam⁴

Affiliation:

1. University of Eswatini (formerly Swaziland),Faculty of Science and Engineering,Department of Computer Science

2. Durban University of Technology,Faculty of Accounting and Informatics,Department of Information,South Africa,4001

3. Teaching and Learning Durban University of Technology,Division of the Deputy Vice-Chancellor,South Africa

4. Women's University of Africa,Faculty of Management and Entrepreneurial Sciences,Harare,Zimbabwe

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9744636/9744642/09744657.pdf?arnumber=9744657

Reference31 articles.

1. Diverging Mobile Technology’s Cognitive Techniques into Tackling Malaria in Sub-Saharan Africa: A Review

2. Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews

3. A novel model for malaria prediction based on ensemble algorithms

4. Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha

5. LSTM based prediction of malaria abundances using big data

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1. Identifying and predicting climate change impact on vector-borne disease using machine learning: Case study of Plasmodium falciparum from Africa;The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences;2024-06-11

2. Leveraging mobile phones to improve malaria healthcare service delivery in Buhera rural communities: implications for practice and policy;Behaviour & Information Technology;2024-06-06

3. Machine Learning Models for Identifying Factors Influencing and Predicting Malaria Among Children Under Five Years in Nigeria;2024 Conference on Information Communications Technology and Society (ICTAS);2024-03-07

4. Implementation of ensemble machine learning classifiers to predict diarrhoea with SMOTEENN, SMOTE, and SMOTETomek class imbalance approaches;2023 Conference on Information Communications Technology and Society (ICTAS);2023-03

5. Application of machine learning techniques for predicting child mortality and identifying associated risk factors;2023 Conference on Information Communications Technology and Society (ICTAS);2023-03

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