AIMC Topic: Decision Trees

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Flight delay prediction: Evaluating machine learning algorithms for enhanced accuracy.

PloS one
Flight delays pose substantial operational and economic challenges for airlines, directly affecting scheduling efficiency, resource allocation, and passenger satisfaction. Accurate prediction of arrival delays is therefore critical for optimizing air...

Construction of the prediction model and analysis of key winning factors in world women's volleyball using gradient boosting decision tree.

Scientific reports
This study aims to analyze the key factors contributing to victories in world women's volleyball matches and predict match win rates using machine learning algorithms. Initially, Grey Relational Analysis (GRA) was employed to analyze the fundamental ...

Prioritization of patients at risk of heart attack using a novel full-objective ITARA based on Random Forest and Decision tree.

Scientific reports
Heart attacks remain a major cause of morbidity and mortality, particularly among middle-aged and older adults, often aggravated by unhealthy lifestyles and limited preventive care. Early identification and prioritization of at-risk individuals are e...

Evaluation of attitudes of university students towards artificial intelligence using data mining methods.

Scientific reports
This study analyzes university students' attitudes towards artificial intelligence. Within the scope of the research, the data obtained from 1379 students through scale application were classified into three classes as "Insufficient", "Sufficient" an...

Speech-based respiratory diagnostics: A study on COVID-19 detection with machine learning.

PloS one
Respiratory sound analysis has emerged as a promising approach for detecting and diagnosing respiratory diseases, including COVID-19. This study investigates using OpenSMILE features for COVID-19 detection using vowel speech sounds /a/, /e/, and /o/ ...

A Feature Extraction and Selection Framework for Electrocorticography-Based Neural Activity Classification.

Journal of medical systems
Electrocorticography (ECoG) signals provide a valuable window into neural activity, yet their complex structure makes reliable classification challenging. This study addresses the problem by proposing a feature-selective framework that integrates mul...

Predicting COVID-19 patient recovery or mortality using deep neural decision tree and forest.

BMC research notes
OBJECTIVE: Identifying patients at high risk of mortality is crucial for emergency physicians to allocate hospital resources effectively, particularly in regions with limited medical services. This need becomes even more pressing during global health...

Machine learning-driven Diabetes Health Tracer (DHT): Optimizing prognosis using RaSK_GraDe and RaSK_GraDeL models.

PloS one
Diabetes mellitus presents a significant global health challenge, particularly in regions like Pakistan, India, and Bangladesh. Machine learning (ML) techniques offer promising solutions for diabetes prediction, surpassing traditional methods in reli...

Predictive Modeling of DNA Damage Outcomes: Classification of Mutational Determinants Using Augmented Machine Learning Techniques.

Chemical research in toxicology
The mutational outcome of DNA damage as a direct result of constant chemical assault is governed by major factors, including the structure and nature of damage, replication, and repair machinery . The role of the size of the adduct, adduct-flanking b...

Enhanced performance in automated diabetic retinopathy diagnosis achieved through Voronoi diagrams and artificial intelligence.

Scientific reports
Diabetic retinopathy (DR), a serious eye condition in diabetic patients, requires early and precise detection for effective treatment. Late diagnosis and poor blood sugar control exacerbate this condition, highlighting the need for improved diagnosti...