AIMC Topic: Machine Learning

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Fine-Scale Spatial Prediction on the Risk of Infection in the Republic of Korea.

Journal of Korean medical science
BACKGROUND: Malaria elimination strategies in the Republic of Korea (ROK) have decreased malaria incidence but face challenges due to delayed case detection and response. To improve this, machine learning models for predicting malaria, focusing on hi...

Mitigating machine learning bias between high income and low-middle income countries for enhanced model fairness and generalizability.

Scientific reports
Collaborative efforts in artificial intelligence (AI) are increasingly common between high-income countries (HICs) and low- to middle-income countries (LMICs). Given the resource limitations often encountered by LMICs, collaboration becomes crucial f...

High-resolution mapping of essential maternal and child health service coverage in Nigeria: a machine learning approach.

BMJ open
BACKGROUND: National-level coverage estimates of maternal and child health (MCH) services mask district-level and community-level geographical inequities. The purpose of this study is to estimate grid-level coverage of essential MCH services in Niger...

PerSEveML: a web-based tool to identify persistent biomarker structure for rare events using an integrative machine learning approach.

Molecular omics
Omics data sets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these data sets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gai...

Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.

Frontiers in endocrinology
OBJECTIVE: The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtr...

Unveiling Immune-related feature genes for Alzheimer's disease based on machine learning.

Frontiers in immunology
The identification of diagnostic and therapeutic biomarkers for Alzheimer's Disease (AD) remains a crucial area of research. In this study, utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we identified RHBDF2 and TNFRSF1...

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.

Frontiers in immunology
Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management...

Understanding sexual homicide in Korea using machine learning algorithms.

Behavioral sciences & the law
The current study was conducted to confirm the characteristics in sexual homicide and to explore variables that effectively differentiate sexual homicide and nonsexual homicide. Further, newer methods that have received attention in criminology, such...

Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma using bioinformatics and machine learning.

PloS one
Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for treatment based on the current NCCN Guidelines. Thus, the classification will be associated with th...