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Introduction. Diabetes is a chronic disease characterized by a high blood glucose level. It can lead to complications that affect the quality of life and increase the costs of healthcare. In recent years, prevalence and mortality rates have increased worldwide. The development of models with high predictive performance can help in the early identification of the disease. Objective. To develope a model based on artificial intelligence to support clinical decisionmaking in the early detection of diabetes. Materials and methods. We conducted a cross-sectional study, using a dataset that contained age, signs, and symptoms of patients with diabetes and of healthy individuals. Pre-processing techniques were applied to the data. Subsequently, we built the model based on fuzzy cognitive maps. Performance was evaluated with three metrics: accuracy, specificity, and sensitivity. Results. The developed model obtained an excellent predictive performance with an accuracy of 95%. In addition, it allowed to identify the behavior of the variables involved using simulated iterations, which provided valuable information about the dynamics of the risk factors associated with diabetes. Conclusions. Fuzzy cognitive maps demonstrated a high value for the early identification of the disease and in clinical decision-making. The results suggest the potential of these approaches in clinical applications related to diabetes and support their usefulness in medical practice to improve patient outcomes.

Malaria is an infection caused by the Plasmodium parasite that has a major epidemiological, social, and economic impact worldwide. Conventional diagnosis of the disease is based on microscopic examination of thick blood smears. This analysis can be time-consuming, which is key to generate prevention strategies and adequate treatment to avoid the complications associated with the disease. To address this problem, we propose a deep learning-based approach to detect not only malaria parasites but also leukocytes to perform parasite/ μ L blood count. We used positive and negative images with parasites and leukocytes. We performed data augmentation to increase the size of the dataset. The YOLOv8 algorithm was used for model training and using the counting formula the parasites were counted. The results showed the ability of the model to detect parasites and leukocytes with 95% and 98% accuracy, respectively. The time spent by the model to report parasitemia is significantly less than the time spent by malaria experts. This type of system would be supportive for areas with poor access to health care. We recommend validation of such approaches on a large scale in health institutions. [ABSTRACT FROM AUTHOR]

Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model that mimics the decision-making process of a medical expert. Our method uses FCMs to model complexity and uncertainty in dengue. The model was evaluated in simulated scenarios with each of the dengue classifications. These maps allow us to represent and process vague and fuzzy information effectively, capturing relationships that often go unnoticed in conventional approaches. The results of the simulations show the potential of our approach to detecting dengue complications. This innovative strategy has the potential to transform the way clinical management of dengue is approached. This research is a starting point for further development of complication detection approaches for events of public health concern, such as dengue. [ABSTRACT FROM AUTHOR]

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Boll weevil is an important pest that affects cotton crops worldwide, causing significant economic losses. The classification of the boll-weevil population is crucial for developing effective pest management strategies. However, the low availability of data and features makes classification a challenging task. This study aimed to investigate the use of Transfer Learning (TL) techniques to improve the classification of boll weevil populations. Three types of TL techniques, instance-based, feature-based, and parameter-based, were studied to improve the classification performance of the machine learning algorithms. This work used data from two domains, one with few instances and the other with few features, to test the proposed approaches. Also, climate variables (temperature, humidity, and rainfall) were incorporated as features to predict the level of the boll-weevil attack. The most relevant results of this work are that define (1) How to measure and quantify the similarity or relationship between tasks of different domains; (2) How to select, align, or adapt the relevant features, instances, or models from the source task/domain to the target task/domain; (3) How to reuse parameter settings from the source domain; and (4) How to evaluate and validate the performance and robustness of the TL model on the target task/domain. The proposed approach achieved significant improvements in classification over previous results in the metrics of accuracy and F-measure. For example, in the case with few instances reached an accuracy of 90.79%, while in the case with few features reached an accuracy of 96.28%. Thus, the results demonstrate the effectiveness of TL techniques in improving the classification of boll-weevil populations in cotton crops when few data and/or features are available. ● The classification of the boll-weevil population for developing effective pest management strategies. ● Transfer Learning (TL) approaches to improve the Insect Pest Classification in Cotton. ● Instance-based, feature-based, and parameter-based techniques for the pest management [ABSTRACT FROM AUTHOR]

The development of computational methodologies to support clinical decision-making is of vital importance to reduce morbidity and mortality rates. Specifically, prescriptive analytic is a promising area to support decision-making in the monitoring, treatment and prevention of diseases. These aspects remain a challenge for medical professionals and health authorities. In this study, we propose a methodology for the development of prescriptive models to support decision-making in clinical settings. The prescriptive model requires a predictive model to build the prescriptions. The predictive model is developed using fuzzy cognitive maps and the particle swarm optimization algorithm, while the prescriptive model is developed with an extension of fuzzy cognitive maps that combines them with genetic algorithms. We evaluated the proposed approach in three case studies related to monitoring (warfarin dose estimation), treatment (severe dengue) and prevention (geohelminthiasis) of diseases. The performance of the developed prescriptive models demonstrated the ability to estimate warfarin doses in coagulated patients, prescribe treatment for severe dengue and generate actions aimed at the prevention of geohelminthiasis. Additionally, the predictive models can predict coagulation indices, severe dengue mortality and soil-transmitted helminth infections. The developed models performed well to prescribe actions aimed to monitor, treat and prevent diseases. This type of strategy allows supporting decision-making in clinical settings. However, validations in health institutions are required for their implementation. • A clinical decision-support system based on prescriptive models. • Methodology for the development of clinical prescriptive models. • Fuzzy cognitive maps and particle swarm optimization algorithm for clinical prescriptive models. • Prescriptive clinical models for the treatment of severe dengue and coagulated patients. • Prescriptive clinical models for geohelminthiasis prevention. [ABSTRACT FROM AUTHOR]