AIMC Topic: Algorithms

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Development of risk models for early detection and prediction of chronic kidney disease in clinical settings.

Scientific reports
Chronic kidney disease (CKD) imposes a high burden with high mortality and morbidity rates. Early detection of CKD is imperative in preventing the adverse outcomes attributed to the later stages. Therefore, this study aims to utilize machine learning...

Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes.

Scientific reports
Currently applicable models for predicting live birth outcomes in patients who received assisted reproductive technology (ART) have methodological or study design limitations that greatly obstruct their dissemination and application. Models suitable ...

Development and validation of a web-based calculator for determining the risk of psychological distress based on machine learning algorithms: A cross-sectional study of 342 lung cancer patients.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: Early and accurate identification of the risk of psychological distress allows for timely intervention and improved prognosis. Current methods for predicting psychological distress among lung cancer patients using readily available data are ...

A topic modeling approach for analyzing and categorizing electronic healthcare documents in Afaan Oromo without label information.

Scientific reports
Afaan Oromo is a resource-scarce language with limited tools developed for its processing, posing significant challenges for natural language tasks. The tools designed for English do not work efficiently for Afaan Oromo due to the linguistic differen...

Dental bur detection system based on asymmetric double convolution and adaptive feature fusion.

Scientific reports
This study aims to improve the detection of dental burs, which are often undetected due to their minuscule size, slender profile, and substantial manufacturing output. The present study introduces You Only Look Once-Dental bur (YOLO-DB), an innovativ...

A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks.

Scientific reports
Multiple sclerosis (MS) is a severe brain disease that permanently destroys brain cells, impacting vision, balance, muscle control, and daily activity. This research employs a weighted combination of deep neural networks and optimization techniques f...

An effective method for detecting the wheat freshness by integrating biophotonics and machine learning algorithm.

Scientific reports
The accurate and timely assessment of wheat freshness is not only a complex scientific endeavor but also a critical aspect of grain storage safety. This study introduces an innovative approach for evaluating wheat freshness by integrating machine lea...

Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders.

Scientific reports
Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a preliminary stage. The most prevalent examination tool for cervical cancer prompt identification is...

Predicting patients' sentiments about medications using artificial intelligence techniques.

Scientific reports
The increasing development of technology has led to the increase of digital data in various fields, such as medication-related texts. Sentiment Analysis (SA) in medication is essential to give clinicians insights into patients' feedback about the tre...

Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT.

Scientific reports
Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significant management implications. We assessed the performance of radiomics-based models in distinguishing ...