In medical sciences, to ascertain the origin of a sickness, professionals utilize their expertise and knowledge to analyze a person's symptoms and indications. These symptoms (indicators) are threshold values that health specialists use to determine the cause of the illness by comparing a specific proportion of measurements to where a healthy population would fall. Consequently, diagnostic mistakes occur as a result of inaccuracy and imprecision. This study utilizes machine learning to categorize haemoglobin variations. Specifically, the data set used in this study includes 752 complete blood count laboratory analyses of adult patients aged eighteen and above obtained from Lagos State University Teaching Hospital (LASUTH). Multiple machine learning methods were utilized for classification from which five of the methods employed were examined and assessed. Comparative analysis was done using the five algorithms (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB)). Contrary to work done by previous researchers, it was observed that the SVM model showed the best classification accuracy of 94.7%, with an F1-score of 94.5%, precision of 94.8%, recall of 94.7%, specificity of 97.3%, and area under curve (AUC) of 99.0%. Among the other models considered, the RF model gave the least accuracy result of 87.4%. The study shows that the support vector machine algorithm outperforms the other classifiers in terms of accuracy when predicting haemoglobin variants given the haematological parameters.

Multi-criteria decision model (MCDM) is used to describe a family of techniques which considers multiple criteria in order to make a choice (among several alternatives). Sometimes, both alternatives and criteria involve qualitative definitions which have to be accounted for. Accordingly, fuzzy TOPSIS is one of several MCDM methods and it serves as a scientific way to solve selection problems that involve uncertainty in criteria definition. This work considers a beverage manufacturing company where selection problem has caused a downturn in production. Inconsistency and unreliability of previous suppliers have caused the company to loose their competitive edge. Fuzzy TOPSIS is proposed to solve the challenge in selecting suppliers of a key raw material. Three decision makers evaluated three suppliers of sugar considering eight criteria for subjective weights based on a 5-point scale. Decision matrices were constructed and normalized for the three submissions. Result obtained showed that supplier 2 had the lowest fuzzy positive ideal solution and the highest fuzzy negative ideal solution. The same supplier had the highest closeness coefficient (0.782) which implies that supplier 2 is the best option.