MALARIA

Malaria remains a global health crisis, particularly in low-resource regions, where traditional diagnostic methods face challenges such as human error, resource constraints, and delayed detection. This project addresses these limitations by leveraging supervised machine learning (ML) to enhance malaria diagnosis and outbreak prediction. The motivation stems from the urgent need for scalable, accurate, and cost-effective solutions to reduce the disease’s burden, which claims over 600,000 lives annually. The objective is to develop robust ML models capable of automating malaria diagnosis using blood smear images and patient metadata while improving outbreak forecasting through environmental and epidemiological data analysis. Methodologically, the study employs supervised learning algorithms, including convolutional neural networks (CNNs) for image- based detection and random forests for tabular data. Datasets were preprocessed to handle class imbalance and missing values, followed by hyperparameter tuning and cross-validation to optimize performance. Results demonstrated that CNNs achieved 96% accuracy in classifying infected blood cells, outperforming traditional methods like microscopy. Random Forest models yielded 92% recall and 89% precision in predicting malaria risk from clinical data, highlighting their utility in early diagnosis. Additionally, stratified k-fold cross-validation ensured model generalizability across diverse datasets. This work underscores the transformative potential of supervised ML in malaria control, offering tools that enhance diagnostic speed, accuracy, and accessibility. By bridging technological innovation with public health needs, the project contributes to global efforts toward malaria eradication, particularly in endemic regions

This study investigated the effects of malaria parasite infection on kidney function using albino Wistar rats. The aim of the study was to determine kidney impairment induced by malaria through controlled infection with Plasmodium berghei, a rodent malaria parasite closely similar to Plasmodium falciparum. Sixteen male Wistar rats (130–174 g) were divided into four groups:
control (uninfected), and three experimental groups infected with high (10⁶ iRBCs), medium (10⁴ iRBCs), and low (10² iRBCs) parasite doses, respectively. At the end of a 42-day experimental period, kidneys were harvested, processed, and examined histologically using hematoxylin and eosin staining. Results revealed dose-dependent renal pathology, with the high infection group showing a tendency of marked glomerular hypertrophy, tubular necrosis, vascular congestion, interstitial inflammatory infiltration, and hemosiderin casts, while moderate and mild changes were observed in the medium and low infection groups. Kidney weights however showed no significant increase in infected rats compared to controls, indicating parasitemia-related organomegaly. These findings demonstrate that malaria infection causes progressive, dose-dependent kidney damage characterized by glomerular and tubular injury, interstitial inflammation, and vascular alterations. In conclusion, malaria-associated nephropathy is a major complication of infection, and Plasmodium berghei-infected Wistar rats provide a reliable model for studying malaria-induced renal dysfunction and for evaluating potential
therapeutic interventions.

This study investigated the prevalence and outcome of malaria infection among children below 11 years of age in a tertiary healthcare facility in Benin City from 2022–2024. The study aimed to determine the months and years when malaria infection was most prevalent, the gender in which malaria infection was most common, the number of children who had malaria infection, and the treatment outcomes of malaria infection among children below 11 years in a selected tertiary healthcare facility from 2022–2024. The study adopted a non-experimental, retrospective
research design method. The study population consisted of all children aged 0 to 10 years who presented with a confirmed diagnosis of malaria at a selected tertiary healthcare facility in Benin City, Edo State, between 2022–2024. A sample size of 900 medical records was selected for the study using a retrospective census sampling technique. A checklist was used for data collection. The instrument was validated by the research supervisor and two other experts in the field. To ensure the reliability of the instrument, a pilot study was conducted prior to full data collection, extracting information from 20 randomly selected pediatric malaria case records not included in the main study. The data collected were analyzed using Statistical Package for Social Sciences (SPSS) version 20 to obtain the Mean, SD, chi-square, and P-value < 0.05. The result shows that malaria was most prevalent in May and in the year 2023, and the treatment outcome of malaria infection had a 95% success rate and a 5% failure rate. Based on the findings, it was recommended that health education by health personnel should be intensified to enlighten parents on the dangers of malaria and ways of preventing it. Mass media outlets such as television, radio, road jingles, and posters should also be used to disseminate useful information on malaria.