AIMC Topic: Machine Learning

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Joint ensemble learning-based risk prediction of Alzheimer's disease among mild cognitive impairment patients.

The journal of prevention of Alzheimer's disease
OBJECTIVE: Due to the recognition for the importance of early intervention in Alzheimer's disease (AD), it is important to focus on prevention and treatment strategies for mild cognitive impairment (MCI). This study aimed to establish a risk predicti...

Fruit wines classification enabled by combing machine learning with comprehensive volatiles profiles of GC-TOF/MS and GC-IMS.

Food research international (Ottawa, Ont.)
Fruit wines, produced through the fermentation of various fruits, are well-documented for their distinct flavor profiles. Intelligent sensory analysis, GC-TOF/MS and GC-IMS were used for the analysis of the volatile profile of eight types of fruit wi...

Combining deep learning and machine learning techniques to track air pollution in relation to vegetation cover utilizing remotely sensed data.

Journal of environmental management
The rapid urban expansion in Dhaka, the capital of Bangladesh, has escalated air pollution levels and led to a significant decrease in green spaces. This study employed machine learning (ML) and deep learning (DL) techniques to examine the relationsh...

REMED-T2D: A robust ensemble learning model for early detection of type 2 diabetes using healthcare dataset.

Computers in biology and medicine
Early diagnosis and timely treatment of diabetes are critical for effective disease management and the prevention of complications. Undiagnosed diabetes can lead to an increased risk of several health issues. Although numerous machine learning (ML) m...

Dynamic blinking feature extraction for automated facial nerve paralysis detection.

Computers in biology and medicine
Facial nerve paralysis (FNP) impair eyelid closure and blinking, risking ophthalmic complications and vision loss. Current detection methods primarily rely on static facial asymmetries, overlooking the dynamic eyelid movements during blinking that ar...

You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction.

Computers in biology and medicine
Breast Cancer is the most commonly diagnosed cancer worldwide. While screening mammography diminishes the burden of this disease, it has some flaws related to the presence of false negatives. Adapting screening to each woman's needs could help overco...

Subjective recovery in professional soccer players: A machine learning and mediation approach.

Journal of sports sciences
Coaches often ask players to judge their recovery status (subjective recovery). We aimed to explore potential determinants of subjective recovery in 101 male professional soccer players of 4 Italian Serie C teams and to further investigate whether th...

Toward Machine Learning Electrospray Ionization Sensitivity Prediction for Semiquantitative Lipidomics in Stem Cells.

Journal of chemical information and modeling
Specificity, sensitivity, and high metabolite coverage make mass spectrometry (MS) one of the most valuable tools in metabolomics and lipidomics. However, translation of metabolomics MS methods to multiyear studies conducted across multiple batches i...

Estimation of Hematocrit Volume Using Blood Glucose Concentration through Extreme Gradient Boosting Regressor Machine Learning Model.

Journal of chemical information and modeling
Lifestyle diseases such as cardiovascular disorders, diabetes, etc. affect the physiological metabolism and become chronic upon negligence. Diabetes is one of the key factors that is interlinked with a plethora of diseases. Health management can be a...

Machine Learning Assisted-Intelligent Lactic Acid Monitoring in Sweat Supported by a Perspiration-Driven Self-Powered Sensor.

Nano letters
Lactic acid has aroused increasing attention due to its close association with serious diseases. A real-time, dynamic, and intelligent detection method is vital for sensitive detection of lactic acid. Here, a machine learning (ML)-assisted perspirati...