AIMC Topic: Decision Trees

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Predicting and Evaluating Cognitive Status in Aging Populations Using Decision Tree Models.

American journal of Alzheimer's disease and other dementias
To improve the identification of cognitive impairment by distinguishing normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). A recursive partitioning tree model was developed using ARMADA data and the NIH Toolbox, a...

The data analysis of sports training by ID3 decision tree algorithm and deep learning.

Scientific reports
In order to improve the accuracy and efficiency of sports training data analysis, this paper proposes an optimized analysis model by combining Iterative Dichotomiser 3 (ID3) decision tree algorithm and deep learning model. As an important scientific ...

Reliable protocol using gradient boosting decision tree with limited experimental data to modify membrane surface for enhanced resilience and nitrogen removal in biofilm system.

Bioresource technology
This study highlights the importance of advanced membrane modification optimization methods for Membrane biofilm reactors (MBfR). Using Response surface methodology (RSM) and Gradient Boosting Decision Tree (GBDT), a relationship between solution con...

The establishment of machine learning prognostic prediction models for pineal region tumors based on SEER-A multicenter real-world study.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: Pineal region tumors (PRT) are rare intracranial neoplasms with diverse pathological types and growth characteristics, leading to varied clinical manifestations. This study aims to develop machine learning (ML) models for survival predict...

Optimizing machine learning models for predicting anemia among under-five children in Ethiopia: insights from Ethiopian demographic and health survey data.

BMC pediatrics
BACKGROUND: Healthcare practitioners require a robust predictive system to accurately diagnose diseases, especially in young children with conditions such as anemia. Delays in diagnosis and treatment can have severe consequences, potentially leading ...

Differentiation of black tea according to country of origin using the μ-CTE/TD/GC-MS method combined with decision tree-optimizable neural network analysis.

Journal of the science of food and agriculture
BACKGROUND: Accurate discrimination of the country of origin of teas is critical to determine their actual commercial value, to meet consumer preferences, and to ensure compliance with labeling regulations. Therefore, in this study, we developed a ne...

A retrospective study using machine learning to develop predictive model to identify rotavirus-associated acute gastroenteritis in children.

PeerJ
BACKGROUND: Rotavirus is the leading cause of severe dehydrating diarrhea in children under 5 years worldwide. Timely diagnosis is critical, but access to confirmatory testing is limited in hospital settings. Machine learning (ML) models have shown p...

Optimized classification of dental implants using convolutional neural networks and pre-trained models with preprocessed data.

BMC oral health
OBJECTIVE: This study evaluates the performance of various classifiers and pre-trained models for dental implant state classification using preprocessed radiography images with masks.

DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants.

BMC bioinformatics
BACKGROUND: A significant challenge in precision medicine is confidently identifying mutations detected in sequencing processes that play roles in disease treatment or diagnosis. Furthermore, the lack of representativeness of single nucleotide varian...

Predicting and investigating water quality index by robust machine learning methods.

Journal of environmental management
This study addresses the critical challenges of waste management and water quality in urban environments, where accelerated urbanization has exacerbated environmental degradation and public health risks. Employing advanced machine learning algorithms...