Browsing by Author "Azamat Serek"

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    Development of method to analyzefactors of kidney disease by the use of fuzzy logic
    (SDU University, 2025) Assel Yembergenova; Azamat Serek; Bauyrzhan Berlikozha
    The study introduces a new strategy for the analysis of kidney disease parameters based on fuzzy logic.Fuzzy logic is a more accurate way to categorize clinical parameters than statistical analysis because thereis uncertainty and variability in medical data. The data is comprised of an extensive amount of clinicalparameters including age, blood pressure, specific gravity, albumin, sugar, random blood glucose, bloodurea, serum creatinine, sodium, potassium, hemoglobin, packed cell volume, white blood cell count, andred blood cell count.The methodology utilizes fuzzy logic centroid computation to categorize these parameters into low,medium, and high levels to provide a more dynamic and interpretable assessment of renal health. Fuzzymemberships give the current work the capability to discover intricate interrelationships between clinicalvariables, which may have been otherwise unattainable by conventional mean, median, and standarddeviation-based analyses.The findings confirm that fuzzy logic and conventional statistical methods enhance the comprehensionof kidney disease by incorporating intricate interactions between clinical variables. The method is employedto achieve more accurate prediction and diagnostic models, offering insight to be used in kidney diseaseassessment and medical decisions.
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    Forecasting Student Academic Performance Using Machine Learning
    (SDU University, 2025) Danial Polat; Azamat Serek
    Educational data mining depends on accurate student academic outcome forecasting to detect studentswho need help early and receive specific support. Traditional linear models have been used extensively yetthey fail to detect the intricate non-linear patterns which exist in student achievement data. The evaluationof machine learning algorithms and their features for student outcome prediction in Portuguese secondaryeducation remains insufficient because of missing systematic assessments. The research investigates howLinear Regression and Random Forest and K-Nearest Neighbors perform when predicting Portugueselanguage grades from 649 student records containing 30 demographic and social and academic attributes.The evaluation of model performance used three established metrics which included Mean Squared Error(MSE) and R-Squared (R²) and Mean Absolute Error (MAE). The results showed Linear Regressionproduced the most accurate predictions through its lowest MSE (9.00) and MAE (2.30) values but its weakR² value (0.01) indicated poor explanatory power. The error rates of Random Forest matched those of LinearRegression (MSE = 9.48 and MAE = 2.34) yet its negative R² (-0.04) indicated poor generalization becauseof irrelevant features and suboptimal hyperparameters. The KNN model showed the worst results (MSE =11.10 and MAE = 2.57 and R² = -0.21) because it failed to detect important patterns without additionaloptimization. The results show that educational prediction tasks require both optimal feature selectionand parameter adjustment for successful results. The research shows that linear models perform betterthan complex methods in specific situations yet optimized non-linear models demonstrate superior abilityto understand student achievement complexity. The research provides essential guidelines for developingbetter feature engineering and machine learning approaches to predict educational results