Tomato plants are highly affected by diverse diseases. A timely and accurate diagnosis plays an important role to prevent the quality of crops. Recently, deep learning (DL), specifically convolutional neural networks (CNNs), have achieved extraordinary results in many applications, including the classification of plant diseases. This work focused on fine-tuning based on the comparison of the state-of-the-art architectures: AlexNet, GoogleNet, Inception V3, Residual Network (ResNet) 18, and ResNet 50. An evaluation of the comparison was finally performed. The dataset used for the experiments is contained by nine different classes of tomato diseases and a healthy class from PlantVillage. The models were evaluated through a multiclass statistical analysis based on accuracy, precision, sensitivity, specificity, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the GoogleNet technique, with 99.72% of AUC and 99.12% of sensitivity. It is possible to conclude that this significantly success rate makes the GoogleNet model a useful tool for farmers in helping to identify and protect tomatoes from the diseases mentioned.

The prevalence of diabetes mellitus is increasing worldwide, causing health and economic implications. One of the principal microvascular complications of type 2 diabetes is Distal Symmetric Polyneuropathy (DSPN), affecting 42.6% of the population in Mexico. Therefore, the purpose of this study was to find out the predictors of this complication. The dataset contained a total number of 140 subjects, including clinical and paraclinical features. A multivariate analysis was constructed using Boruta as a feature selection method and Random Forest as a classification algorithm applying the strategy of K-Folds Cross Validation and Leave One Out Cross Validation. Then, the models were evaluated through a statistical analysis based on sensitivity, specificity, area under the curve (AUC) and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model with this approach, presenting 67% of AUC with only three features as predictors. It is possible to conclude that this proposed methodology can classify patients with DSPN, obtaining a preliminary computer-aided diagnosis tool for the clinical area in helping to identify the diagnosis of DSPN.

Early diagnosis and preventive measures are necessary to mitigate diseases’ impact on the yield of Colocasia esculenta (Taro). This study addresses the challenges of Taro disease identification by employing two key strategies: integrating explainable artificial intelligence techniques to interpret deep learning models and conducting a comparative analysis of advanced architectures Inception V3, ResNet-50, and Vision Transformers for classifying common Taro diseases, including leaf blight and mosaic virus, as well as identifying healthy leaves. The novelty of this work lies in the first-ever integration of SHapley Additive exPlanations (SHAP) with deep learning architectures to enhance model interpretability while providing a comprehensive comparison of state-of-the-art methods for this underexplored crop. The proposed models significantly improve the ability to recognize complex patterns and features, achieving high accuracy and robust performance in disease classification. The model’s efficacy was evaluated through multi-class statistical metrics, including accuracy, precision, F1 score, recall, specificity, Chohen’s kappa, and area under the curve. Among the architectures, Inception V3 exhibited superior performance in accuracy (0.9985), F1 score (0.9985), recall (0.9985), and specificity (0.9992). The explainability of Inception V3 was further enhanced using SHAP, which provides insights by dissecting the contributions of individual features in Taro leaves to the model’s predictions. This approach facilitates a deeper understanding of the disease classification process and supports the development of effective disease management strategies, ultimately contributing to improved Taro cultivation practices.

Background/Objectives: The increase in patients with type 2 diabetes, coupled with the development of complications caused by the same disease is an alarming aspect for the health sector. One of the main complications of diabetes is nephropathy, which is also the main cause of kidney failure. Once diagnosed, in Mexican patients the kidney damage is already highly compromised, which is why acting preventively is extremely important. The aim of this research is to compare distinct methodologies of feature selection to identify discriminant risk factors that may be beneficial for early treatment, and prevention. Methods: This study focused on evaluating a Mexican dataset collected from 22 patients containing 32 attributes. To reduce the dimensionality and choose the most important variables, four feature selection algorithms: Univariate, Boruta, Galgo, and Elastic net were implemented. After selecting suitable features detected by the methodologies, they are included in the random forest classifier, obtaining four models. Results: Galgo with Random Forest achieved the best performance with only three predictors, “creatinine”, “urea”, and “lipids treatment”. The model displayed a moderate classification performance with an area under the curve of 0.80 (±0.3535 SD), a sensitivity of 0.909, and specificity of 0.818. Conclusions: It is demonstrated that the proposed methodology has the potential to facilitate the prompt identification of nephropathy and non-nephropathy patients, and thereby could be used in the clinical area as a preliminary computer-aided diagnosis tool.

One of the main microvascular complications presented in the Mexican population is diabetic retinopathy which affects 27.50% of individuals with type 2 diabetes. Therefore, the purpose of this study is to construct a predictive model to find out the risk factors of this complication. The dataset contained a total of 298 subjects, including clinical and paraclinical features. An analysis was constructed using machine learning techniques including Boruta as a feature selection method, and random forest as classification algorithm. The model was evaluated through a statistical test based on sensitivity, specificity, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model obtaining 69% of AUC. Moreover, a risk evaluation was incorporated to evaluate the impact of the predictors. The proposed method identifies creatinine, lipid treatment, glomerular filtration rate, waist hip ratio, total cholesterol, and high density lipoprotein as risk factors in Mexican subjects. The odds ratio increases by 3.5916 times for control patients which have high levels of cholesterol. It is possible to conclude that this proposed methodology is a preliminary computer-aided diagnosis tool for clinical decision-helping to identify the diagnosis of DR.

Background: Type 2 diabetes (T2D) is considered a global pandemic by the World Health Organization (WHO), with a growing prevalence, particularly in Mexico. Accurate early diagnosis remains a challenge, especially when accounting for biological sex-based differences. Purpose: This study aims to enhance the classification of T2D in the Mexican population by applying sex-specific ensemble models combined with genetic algorithm-based feature selection. Materials and Methods: A dataset of 1787 Mexican patients (895 females, 892 males) is analyzed. Data are split by sex, and feature selection is performed using GALGO, a genetic algorithm-based tool. Classification models including Random Forest, K-Nearest Neighbor, Support Vector Machine, and Logistic Regression are trained and evaluated. Ensemble stacking models are constructed separately for each sex to improve classification performance. Results: The male-specific ensemble model achieved 94% specificity and 96% sensitivity, while the female-specific model reached 96% specificity and 90% sensitivity. Both models demonstrated strong overall performance. Conclusion: The proposed sex-specific ensemble models represent a clinically valuable approach to personalized T2D diagnosis. By identifying sex-specific predictive features, this work supports the development of precision medicine tools tailored to the Mexican population. This contributes to improving diagnostic precision and supporting more equitable and personalized approaches in clinical settings.