Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification

Algorithms to define diabetes type using data from administrative databases: A systematic review of the evidence

AIMS

To find the best-performing algorithms to distinguish type 1 and type 2 diabetes in administrative data.

METHODS

Embase and MEDLINE databases were searched from January 2000 until January 2023. Papers evaluating the performance of algorithms to define type 1 and type 2 diabetes by reporting diagnostic metrics against a range of reference standards were selected. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies.

RESULTS

Of the 24 studies meeting the eligibility criteria, 19 demonstrated a low risk of bias and low concerns about the applicability of the study population across all domains. Algorithms considering multiple diabetes diagnostic codes alone were sensitive and specific approaches to classify diabetes type (both metrics >92.1% for type 1 diabetes; >86.9% for type 2 diabetes). Among the top 10-performing algorithms to detect type 1 and type 2 diabetes, 70% and 100% featured multiple criteria, respectively. Information on insulin use was more sensitive and specific for detecting diabetes type than were criteria based on use of oral hypoglycaemic agents.

CONCLUSIONS

Algorithms based on multiple diabetes diagnostic codes and insulin use are the most accurate approaches to distinguish type 1 from type 2 diabetes using administrative data. Approaches with more than one criterion may also increase sensitivity in distinguishing diabetes type.

Predictors for the Longevity of People with Diabetes in Buno Bedele and Illubabor Zones, South-west Ethiopia

Introduction

Currently, diabetes is a global health problem and it affects many people, especially in the developing continents. As patients' living conditions improve and the science of medicine advances, the longevity of such patients has increased greatly. Therefore, the purpose of this study was to identify predictors for the association of the longevity of people with diabetes in Buno Bedele and Illubabor Zones, South-west Ethiopia.

Methods

The study applied a retrospective cohort study design approach. In particular, long rank tests for longevity experience and Cox semi-parametric regression were implemented to compare and investigate the predictors associated with the longevity of patients with diabetes.

Results

Among all the patients who participated in this study, 56.9% were females and the rest were males. From the Cox regression result, age (AHR = 1.0550, 95% CI: (1.0250, 1.0860), p-value = 0.001), female patients (AHR = 0.2200, 95% CI: (0.0390, 0.5290)), rural patients (AHR = 0.2200, 95% CI: (0.1000, 0.4890), p-value = 0.001), the existence of fasting blood glucose complication (AHR = 1.2040, 95% CI: (1.0930, 1.4460), p-value = 0.001), the existence of blood pressure (AHR = 1.2480, 95% CI: (1.1390, 1.5999), p-value = 0.0180), treatment type, Sulfonylureas (AHR = 4.9970, 95% CI: (1.4140, 17.6550), p-value = 0.0120), treatment type, Sulfonylurea and Metformin (AHR = 5.7200, 95% CI: (1.7780, 18.3990), p-value = 0.0030) were significantly affected the longevity of people with diabetes.

Conclusion

The findings of the current study showed that the patient's age, sex of patients, residence area, the existence of complications, existence of pressure, and treatment type were major risk factors related to the longevity of people with diabetes. Hence, health-related education should be given to patients who come to take treatment to have better longevity for people with diabetes. More attention should be given to aged patients, male and urban patients, patients under complication treatment, and patients under treatment with single-treatment medication.

Semi-Supervised Instance-Segmentation Model for Feature Transfer Based on Category Attention

In the task of image instance segmentation, semi-supervised instance segmentation algorithms have received constant research attention over recent years. Among these algorithms, algorithms based on transfer learning are better than algorithms based on pseudo-label generation in terms of segmentation performance, but they can not make full use of the relevant characteristics of source tasks. To improve the accuracy of these algorithms, this work proposes a semi-supervised instance segmentation model AFT-Mask (attention-based feature transfer Mask R-CNN) based on category attention. The AFT-Mask model takes the result of object-classification prediction as "attention" to improve the performance of the feature-transfer module. In detail, we designed a migration-optimization module for connecting feature migration and classification prediction to enhance segmentation-prediction accuracy. To verify the validity of the AFT-Mask model, experiments were conducted on two types of datasets. Experimental results show that the AFT-Mask model can achieve effective knowledge transfer and improve the performance of the benchmark model on semi-supervised instance segmentation.

Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network

In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm could aid routine diagnostics. We propose an area-based annotation approach generalized by a new rule template to accurately solve high-resolution biological segmentation tasks in a time-efficient way. These algorithm and implementation rules provide an alternative solution for pathologists to make decisions as accurate as manually. This research is based on an individual database from Semmelweis University, containing 291 high-resolution, bright field microscopy breast tumor tissue images. A total of 70% of the 128 × 128-pixel resolution images (206,174 patches) were used for training a convolutional neural network to learn the features of normal and tumor tissue samples. The evaluation of the small regions results in high-resolution histopathological image segmentation; the optimal parameters were calculated on the validation dataset (29 images, 10%), considering the accuracy and time factor as well. The algorithm was tested on the test dataset (61 images, 20%), reaching a 99.10% f1 score on pixel level evaluation within 3 min on average. Besides the quantitative analyses, the system’s accuracy was measured qualitatively by a histopathologist, who confirmed that the algorithm was also accurate in regions not annotated before.

Binary Horse Optimization Algorithm for Feature Selection

The bio-inspired research field has evolved greatly in the last few years due to the large number of novel proposed algorithms and their applications. The sources of inspiration for these novel bio-inspired algorithms are various, ranging from the behavior of groups of animals to the properties of various plants. One problem is the lack of one bio-inspired algorithm which can produce the best global solution for all types of optimization problems. The presented solution considers the proposal of a novel approach for feature selection in classification problems, which is based on a binary version of a novel bio-inspired algorithm. The principal contributions of this article are: (1) the presentation of the main steps of the original Horse Optimization Algorithm (HOA), (2) the adaptation of the HOA to a binary version called the Binary Horse Optimization Algorithm (BHOA), (3) the application of the BHOA in feature selection using nine state-of-the-art datasets from the UCI machine learning repository and the classifiers Random Forest (RF), Support Vector Machines (SVM), Gradient Boosted Trees (GBT), Logistic Regression (LR), K-Nearest Neighbors (K-NN), and Naïve Bayes (NB), and (4) the comparison of the results with the ones obtained using the Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), and Binary Crow Search Algorithm (BCSA). The experiments show that the BHOA is effective and robust, as it returned the best mean accuracy value and the best accuracy value for four and seven datasets, respectively, compared to BGWO, BPSO, and BCSA, which returned the best mean accuracy value for four, two, and two datasets, respectively, and the best accuracy value for eight, seven, and five datasets, respectively.