Determinants of Childhood Vaccination Uptake: A Machine Learning Approach Using a Decision Tree Classifier

Khamisi Kalegele — 2025-05-19

This study applies a Decision Tree Classifier to determine the most influential factors affecting childhood vaccination uptake in Tanzania. Methodologically, the study was quantitative in nature, using the power of Decision Support Classifier in decision making. As a result of applying a Decision Tree Classifier, six key predictors were identified as influential to the model: birth interval, number of antenatal visits, maternal age, child age, number of births in the last five years, and number of children under five years old. Among these, birth interval (33%) and antenatal visits (13%) had the highest impact on vaccination uptake. The findings indicate that shorter birth intervals, fewer antenatal visits, and younger maternal age reduce the likelihood of child vaccination. Additionally, children below 18 months and those from large households are more likely to miss immunisation. The study underscores the importance of family planning, antenatal education, healthcare access, and targeted immunisation campaigns to improve vaccination rates. These insights provide data-driven recommendations for policymakers and healthcare professionals to enhance childhood immunisation programs and reduce preventable child mortality.

Using Fishing Information to Enhance Climate Change Adaptation Strategies among Small-Scale Fishers in Pangani District, Tanzania

HENRY Mungongo — 2025-05-19

Climate change is posing harmful impacts on fishing resources as coastal communities have been relying on it to gain their livelihoods. Therefore, the study aimed to assess the use of fishing information for the adaptation of small-scale fishers to climate change in Pangani –Tanzania. The study adopted a mixed approach with the use of purposive and random sampling techniques in selecting 94 respondents. Questionnaires survey and interviews were used to collect primary data. Statistical package for social sciences software was used to analyse quantitative data while content analysis was used to analyse qualitative data. The study revealed the use of social networks and fishermen’s institutions for increasing resilience to climate change. Accessing fishing information through, own experience, advertisement of climate change issues through Television/Radio and Tanzania Metrological agency were the main methods. These main sources provided fishing information on the better use and access of affordable fishing gears, weather changes, improved new fish species, early-maturing fish/fish varieties, fishing calendar changes and environmental conservation methods. Therefore, the study concludes that small-scale fisher’s ability to respond to changing climate is largely dependent on the extent of access and use of fishing information. Therefore, it is recommended that collective strategic initiatives are needed to promote small-scale fishing information through enhanced using of social media extension services, regular village meetings and good applied research institutions, which increases fisherman’s resilience to climate change.

The Journal of Informatics

Determinants of Childhood Vaccination Uptake: A Machine Learning Approach Using a Decision Tree Classifier

Using Fishing Information to Enhance Climate Change Adaptation Strategies among Small-Scale Fishers in Pangani District, Tanzania