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    Automatic Classification of Electrical Complaints Using Decision Trees and Random Forest: A Case Study Applied to CNEL EP
    (Salesian Polytechnic University of Ecuador, 2026-01-01)
    CARPIO VELASCO, FRANCISCO JAVIER  
    ;
    Gloria Margarita Garcés Beltrán
    This study presents an automated classification system to prioritize electrical service complaints at CNEL EP. A total of 143,113 real records were processed through data cleaning, missing-value imputation, and the engineering of predictive variables reflecting complaint urgency and recurrence. Based on these criteria, the target variable “Priority” was defined to distinguish high-priority from normal complaints. Supervised learning models, specifically Decision Tree and Random Forest, were then trained using one-hot encoding and cross-validation. Random Forest delivered the best performance, achieving 91% accuracy and an AUC-ROC of 0.89. These results indicate that the proposed system can significantly improve technical resource allocation and reduce response times for electrical complaints. Moreover, the study demonstrates the feasibility of integrating machine-learning techniques into the operational management of electric distribution companies, enabling future enhancements and real-time deployment.
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    Alzheimer’s Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery
    (MDPI AG, 2025-01-24)
    DOMÍNGUEZ GORTAIRE, JOSÉ  
    ;
    Alejandra Ruiz
    ;
    Ana Belen Porto Pazos
    ;
    Santiago Rodriguez-Yanez
    ;
    Francisco Cedron
    Alzheimer’s disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements have shifted the focus from the traditionally dominant amyloid hypothesis toward a multifactorial understanding of the disease. Emerging evidence suggests that while amyloid-beta (Aβ) accumulation is central to AD, it may not be the primary driver but rather part of a broader pathogenic process. Novel hypotheses have been proposed, including the role of tau protein abnormalities, mitochondrial dysfunction, and chronic neuroinflammation. Additionally, the gut–brain axis and epigenetic modifications have gained attention as potential contributors to AD progression. The limitations of existing therapies underscore the need for innovative strategies. This study explores the integration of machine learning (ML) in drug discovery to accelerate the identification of novel targets and drug candidates. ML offers the ability to navigate AD’s complexity, enabling rapid analysis of extensive datasets and optimizing clinical trial design. The synergy between these themes presents a promising future for more effective AD treatments.
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    Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis
    (Frontiers Media SA, 2025-01-17)
    Feras Al-Obeidat
    ;
    Wael Hafez
    ;
    Asrar Rashid
    ;
    Mahir Khalil Jallo
    ;
    Munier Gador
    Background: Leukemia is the 11th most prevalent type of cancer worldwide, with acute myeloid leukemia (AML) being the most frequent malignant blood malignancy in adults. Microscopic blood tests are the most common methods for identifying leukemia subtypes. An automated optical image-processing system using artificial intelligence (AI) has recently been applied to facilitate clinical decision-making. Aim: To evaluate the performance of all AI-based approaches for the detection and diagnosis of acute myeloid leukemia (AML). Methods: Medical databases including PubMed, Web of Science, and Scopus were searched until December 2023. We used the “metafor” and “metagen” libraries in R to analyze the different models used in the studies. Accuracy and sensitivity were the primary outcome measures. Results: Ten studies were included in our review and meta-analysis, conducted between 2016 and 2023. Most deep-learning models have been utilized, including convolutional neural networks (CNNs). The common- and random-effects models had accuracies of 1.0000 [0.9999; 1.0001] and 0.9557 [0.9312, and 0.9802], respectively. The common and random effects models had high sensitivity values of 1.0000 and 0.8581, respectively, indicating that the machine learning models in this study can accurately detect true-positive leukemia cases. Studies have shown substantial variations in accuracy and sensitivity, as shown by the Q values and I2 statistics. Conclusion: Our systematic review and meta-analysis found an overall high accuracy and sensitivity of AI models in correctly identifying true-positive AML cases.