Predicting postpartum female sexual interest/arousal disorder via adiponectin and biopsychosocial factors: a cohort-based decision tree study.

After childbirth, women experience significant psychological, physiological, and hormonal changes. To better diagnose individuals at risk of postpartum complications, predictive models utilizing data mining and machine learning techniques can be instrumental. The C4.

Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning.

Detecting clinical keratoconus (KCN) poses a challenging and time-consuming task. During the diagnostic process, ophthalmologists are required to review demographic and clinical ophthalmic examinations in order to make an accurate diagnosis. This study aims to develop and evaluate the accuracy of deep convolutional neural network (CNN) models for the detection of keratoconus (KCN) using corneal topographic maps.

Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles.

Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing these parameters in a laboratory setting is both costly and time-consuming.

Optimizing nanoliposomal formulations: Assessing factors affecting entrapment efficiency of curcumin-loaded liposomes using machine learning.

Background: Curcumin faces challenges in clinical applications due to its low bioavailability and poor water solubility. Liposomes have emerged as a promising delivery system for curcumin. This study aims to apply ensemble learning, a machine learning technique, to determine the most effective experimental conditions for formulating stable curcumin-loaded liposomes with a high entrapment efficiency (EE).

Corrigendum to "A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm".

[This corrects the article DOI: 10.1155/2021/1203726.].

Predicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Study.

Background: With the growing rate of cesarean sections, rising morbidity and mortality thereafter is an important health issue. Predictive models can identify individuals with a higher probability of cesarean section, and help them make better decisions. This study aimed to investigate the biopsychosocial factors associated with the method of childbirth and designed a predictive model using the decision tree C4.

A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm.

Missing data occurs in all research, especially in medical studies. Missing data is the situation in which a part of research data has not been reported. This will result in the incompatibility of the sample and the population and misguided conclusions.

Predicting the incidence of COVID-19 using data mining.

Background: The high prevalence of COVID-19 has made it a new pandemic. Predicting both its prevalence and incidence throughout the world is crucial to help health professionals make key decisions. In this study, we aim to predict the incidence of COVID-19 within a two-week period to better manage the disease.

Spatial-time analysis of cardiovascular emergency medical requests: enlightening policy and practice.

Background: Response time to cardiovascular emergency medical requests is an important indicator in reducing cardiovascular disease (CVD) -related mortality. This study aimed to visualize the spatial-time distribution of response time, scene time, and call-to-hospital time of these emergency requests. We also identified patterns of clusters of CVD-related calls.

Predictive model for survival in patients with gastric cancer.

Background And Aim: Gastric cancer is one of the most prevalent cancers in the world. Characterized by poor prognosis, it is a frequent cause of cancer in Iran. The aim of the study was to design a predictive model of survival time for patients suffering from gastric cancer.

Altered Expression of Cell Cycle Regulators in Adult T-Cell Leukemia/ Lymphoma Patients.

Background: Adult T-cell leukemia/lymphoma (ATLL) is caused by human T-cell lymphotropic virus type-1 (HTLV-1). HTLV-1 oncogenes can induce malignancy through controlled gene expression of cell cycle checkpoints in the host cell. HTLV-I genes play a pivotal role in overriding cell cycle checkpoints and deregulate cellular division.

Providing an imputation algorithm for missing values of longitudinal data using Cuckoo search algorithm: A case study on cervical dystonia.

Background: Missing values in data are found in a large number of studies in the field of medical sciences, especially longitudinal ones, in which repeated measurements are taken from each person during the study. In this regard, several statistical endeavors have been performed on the concepts, issues, and theoretical methods during the past few decades.

Methods: Herein, we focused on the missing data related to patients excluded from longitudinal studies.