Patients with beta-thalassaemia major (β-TM) who get regular blood transfusions are at risk of iron overload and hepatitis C virus (HCV) infection. These double injuries together can lead to chronic liver damage. Treatment with pegylated interferon combined ribavirin (Peg-IFN/RBV) is associated with side effects that compromise the patients' quality of life. The efficacy of two anti-viral regimens (Peg-IFN/RBV) and Peg-IFN monotherapy were assessed using a machine learning model to identify patients who could achieve sustained virologic response (SVR) with HCV eradication. This paper is a follow-up study of our previous published paper that used a different method to address the same research question. A hybrid Neuro-SVM model was developed to improve the accuracy of classification that shows 98.83% in group 1 and 99.75 in group 2 and conveyed as a graphical user interface that can help the clinical support decision in the prediction of optimal treatment response. The model was compared to artificial neural network (ANN), support vector machine (SVM) and naïve Bayesian (NB). Using the hybrid model, it would be useful if we distinguish in advance those patients who may benefit from the approved direct anti-viral agents (DAAs) therapy from those who would not. Keywords Beta-thalassemia major • Synthetic minority oversampling • Machine learning • Artificial neural networks All authors assisted with manuscript preparation and revisions. AH and AA designed the study; SK provides the dataset with laboratory follow-up of enrolled patients. AH analyzed the data and wrote the manuscript. All authors read and approved the final version of the manuscript.
Diabetes type 2 (T2DM) is a common chronic disease, increasingly leading to many complications and affecting vital organs. Hyperglycemia is the main characteristic caused by insufficient insulin secretion and poses a serious risk to human health. The objective is to construct a type-2 diabetes prediction model with high classification accuracy. Advanced machine learning and predictive model techniques are utilized to achieve cutting-edge techniques for the early diagnosis of diabetes. This paper proposes an efficient performance model to predict and classify the minority class of type-2 diabetes. The impact of oversampling and undersampling approaches to reduce the effect of an unbalanced class has been compared to classification performance algorithms. Synthetic Minority Oversampling (SMOTE) and Tomek-links techniques are applied and examined. The outcomes were then compared to the original unbalanced dataset using an artificial neural network (ANN) predictive model. The model is compared with other state-of-the-art classifiers such as support vector machine (SVM), random forest (RF), and decision tree (DT). The tuned model had the best accuracy of 92.2%. The experimental findings clearly manifest the improvement in accuracy and evaluation metrics in terms of AUC and F1-measure using the SMOTE oversampling strategy rather than the baseline and undersampling schemes. The study recommends adopting dynamic hyperparameter optimization to further improve accuracy.
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