AIMC Topic: Tanzania

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Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania.

BMC pregnancy and childbirth Apr 1, 2022
BACKGROUND: Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes. We set out to investigate important attributes and train popular machine learning (ML) algorit...
Apgar Score Pregnancy Infant, Newborn Female Tertiary Care Centers Tanzania Delivery, Obstetric Humans Machine Learning Labor, Induced
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Validating machine learning models for the prediction of labour induction intervention using routine data: a registry-based retrospective cohort study at a tertiary hospital in northern Tanzania.

BMJ open Dec 2, 2021
OBJECTIVES: We aimed at identifying the important variables for labour induction intervention and assessing the predictive performance of machine learning algorithms.
Retrospective Studies Pregnancy Humans Machine Learning Registries Labor, Induced Tanzania Female Tertiary Care Centers
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Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis.

Malaria journal Oct 7, 2019
BACKGROUND: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy couple...
Malaria, Falciparum Tanzania Spectrophotometry, Infrared Dried Blood Spot Testing Plasmodium falciparum Supervised Machine Learning Humans Logistic Models
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Machine Learning Model for Imbalanced Cholera Dataset in Tanzania.

TheScientificWorldJournal Jul 25, 2019
Cholera epidemic remains a public threat throughout history, affecting vulnerable population living with unreliable water and substandard sanitary conditions. Various studies have observed that the occurrence of cholera has strong linkage with enviro...
World Health Organization Databases as Topic Rain Cholera Tanzania Machine Learning Seasons Humans
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Prenatal exposure to persistent organic pollutants in Northern Tanzania and their distribution between breast milk, maternal blood, placenta and cord blood.

Environmental research Dec 18, 2018
Human exposure to persistent organic pollutants (POPs) begins during pregnancy and may cause adverse health effects in the fetus or later in life. The present study aimed to assess prenatal POPs exposure to Tanzanian infants and evaluate the distribu...
Prenatal Exposure Delayed Effects Milk, Human Female Family Placenta Humans Environmental Monitoring Infant Polychlorinated Biphenyls Environmental Pollutants Pregnancy Kenya Fetal Blood Maternal Exposure Tanzania
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Implementing an artificial intelligence system into a diabetic eye screening programme in Tanzania.

Transactions of the Royal Society of Tropical Medicine and Hygiene Jun 5, 2025
Tanzania has the highest age-adjusted prevalence of diabetes in sub-Saharan Africa. Diabetic retinopathy, a common complication, is a significant cause of vision loss; but with effective screening and treatment this often can be prevented. However, w...
Diabetic Retinopathy Artificial Intelligence Middle Aged Humans Mass Screening Female Tanzania Male
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Combining satellite imagery and machine learning to predict poverty.

Science (New York, N.Y.) Aug 19, 2016
Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumpt...
Humans Machine Learning Poverty Income Nigeria Malawi Uganda Satellite Imagery Tanzania Rwanda Developing Countries
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