Adherence to the management of type i diabetes among Palestinian patients in Nablus city: a cross-sectional study

The purpose of this study is to investigate the adherence to the management of Type I Diabetes and to investigate factors associated with non-adherence among Palestinian Type 1 Diabetes patients. One hundred and twenty-six patients diagnosed with Type 1 Diabetes were enrolled in an observational cross-sectional study. Diabetes self-care adherence was measured using the Self Care Inventory (SCI). The patients were recruited from a diabetes clinic in Nablus city in Palestine. One-way ANOVA test and simple linear regressions were used in the statistical analysis. Participants age ranged from 3-43 years; 56% of them were females. The mean age at diagnosis for them was 10 years (+/-6.25). The mean glycosylated hemoglobin (A1C) was 9 +/-2.32. 66% of patients reported significant non-adherence to glucose testing, 89% reported non-adherence to diet recommendations, 79% reported non-adherence to exercise, and 21% reported non-adherence to administering insulin on time. Age (r = 0.29, P < 0.05), A1C (r = 0.21, P < 0.05), sex (P < 0.05), and patient educational level (P< 0.05) were significantly related to adherence score. Adherence to treatment among patients with Type 1 Diabetes is poor and is associated with age, sex, A1C, and patient educational level. Designed education programs should be implemented among patients with Type 1 Diabetes, which address the importance of adherence to the management of the diseases. More strategies should focus on monitoring the diet and insulin administration. © 2022, An-Najah National University. All rights reserved

Memory-Based Sand Cat Swarm Optimization for Feature Selection in Medical Diagnosis

The rapid expansion of medical data poses numerous challenges for Machine Learning (ML) tasks due to their potential to include excessive noisy, irrelevant, and redundant features. As a result, it is critical to pick the most pertinent features for the classification task, which is referred to as Feature Selection (FS). Among the FS approaches, wrapper methods are designed to select the most appropriate subset of features. In this study, two intelligent wrapper FS approaches are implemented using a new meta-heuristic algorithm called Sand Cat Swarm Optimizer (SCSO). First, the binary version of SCSO, known as BSCSO, is constructed by utilizing the S-shaped transform function to effectively manage the binary nature in the FS domain. However, the BSCSO suffers from a poor search strategy because it has no internal memory to maintain the best location. Thus, it will converge very quickly to the local optimum. Therefore, the second proposed FS method is devoted to formulating an enhanced BSCSO called Binary Memory-based SCSO (BMSCSO). It has integrated a memory-based strategy into the position updating process of the SCSO to exploit and further preserve the best solutions. Twenty one benchmark disease datasets were used to implement and evaluate the two improved FS methods, BSCSO and BMSCSO. As per the results, BMSCSO acted better than BSCSO in terms of fitness values, accuracy, and number of selected features. Based on the obtained results, BMSCSO as a FS method can efficiently explore the feature domain for the optimal feature set