Artificial Intelligence in Determining Optimal Questions in Assessing Social Economic Status of Individuals for Routine Immunization Services in Tanzania.

Paper Title:
Artificial Intelligence in Determining Optimal Questions in Assessing Social Economic Status of Individuals for Routine Immunization Services in Tanzania.

Authors:
Deogratias Mzurikwao, Lwidiko Edward Mhamilawa, Daudi Simba, Belinda Balandya, Evelyne Assenga, Charles Okanda Nyatega, Jonathan Zeramula, Seif Wibonela, Zacharia Mzurikwao, Bruno Sunguya, 
Muhimbili University of Health and Allied Sciences, Tanzania, Mbeya University of Science and Technology, Tanzania, Emerging Technologies for Health lab (ETH)-MUHAS, Tanzania, Tanzania Atomic Energy Commission (TAEC), Tanzania

Abstract:
This study aimed to determine the lowest optimal questions that could accurately determine the socio-economic status (SES) score of the participants and determine their validity when compared to the standard wealth index. Principal Component Analysis (PCA), Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) techniques were applied using DHS wealth index as the gold standard. Eight DHS questions were found to be optimal for assessing household’s SES with high sensitivity (76.9%) and specificity (94.2%). The correlation with DHS standard wealth index was R2 = 0.76. The study has also shown the potential for using CNN as a method to identify valid questions that can be applied in other domains. Our findings open the possibility of using SES as one of the factors to determine access and completion of routine immunization services. This is important in identifying and targeting populations at risk to enable focussed interventions to increase vaccine coverage.

Keywords:
Artificial Intelligence, Social economic status, PCA, ANN, CNN. 

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