AIMC Topic: Decision Trees
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Advancing ensemble learning techniques for residential building electricity consumption forecasting: Insight from explainable artificial intelligence.
PloS one
Nov 14, 2024
Accurate electricity consumption forecasting in residential buildings has a direct impact on energy efficiency and cost management, making it a critical component of sustainable energy practices. Decision tree-based ensemble learning techniques are p...
Recognizing and explaining driving stress using a Shapley additive explanation model by fusing EEG and behavior signals.
Accident; analysis and prevention
Nov 12, 2024
Driving stress is a critical factor leading to road traffic accidents. Despite numerous studies that have been conducted on driving stress recognition, most of them only focus on accuracy improvement without taking model interpretability into account...
Evaluating Advanced Machine Learning Models for Histopathological Diagnosis of Hansen Disease.
The American Journal of dermatopathology
Nov 5, 2024
INTRODUCTION: Leprosy is a neglected infectious disease caused by Mycobacterium leprae and Mycobacterium lepromatosis and remains a public health challenge in tropical regions. Therefore, the development of technological tools such as machine learnin...
ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction.
Journal of chemical information and modeling
Nov 5, 2024
The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature representation,...
Common laboratory results-based artificial intelligence analysis achieves accurate classification of plasma cell dyscrasias.
PeerJ
Nov 4, 2024
BACKGROUND: Plasma cell dyscrasias encompass a diverse set of disorders, where early and precise diagnosis is essential for optimizing patient outcomes. Despite advancements, current diagnostic methodologies remain underutilized in applying artificia...
Using machine learning to classify temporomandibular disorders: a proof of concept.
Journal of applied oral science : revista FOB
Nov 4, 2024
BACKGROUND: the escalating influx of patients with temporomandibular disorders and the challenges associated with accurate diagnosis by non-specialized dental practitioners underscore the integration of artificial intelligence into the diagnostic pro...
Using interpretable machine learning methods to identify the relative importance of lifestyle factors for overweight and obesity in adults: pooled evidence from CHNS and NHANES.
BMC public health
Nov 1, 2024
BACKGROUND: Overweight and obesity pose a huge burden on individuals and society. While the relationship between lifestyle factors and overweight and obesity is well-established, the relative contribution of specific lifestyle factors remains unclear...
An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates.
Scientific reports
Nov 1, 2024
This study aimed to construct a high-performance prediction and diagnosis model for type 2 diabetic retinopathy (DR) and identify key correlates of DR. This study utilized a cross-sectional dataset of 3,000 patients from the People's Liberation Army ...
Developing a machine learning model with enhanced performance for predicting COVID-19 from patients presenting to the emergency room with acute respiratory symptoms.
IET systems biology
Oct 29, 2024
Artificial Intelligence is playing a crucial role in healthcare by enhancing decision-making and data analysis, particularly during the COVID-19 pandemic. This virus affects individuals across all age groups, but its impact is more severe on the elde...
Accuracy of distinguishing benign, high-risk lesions and malignancies with inductive machine learning models in BIRADS 4 and BIRADS 5 lesions on breast MR examinations.
European journal of radiology
Oct 24, 2024
RATIONALE AND OBJECTIVES: The aim of this study is to explore the utility of Inductive Decision Tree models (IDTs) in distinguishing between benign, malignant, and high-risk (B3) breast lesions.
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