A multi-dimensional student performance prediction model (MSPP): An advanced framework for accurate academic classification and analysis.

Forecasting student performance with precision in the educational space is paramount for creating tailor-made interventions capable to boost learning effectiveness. It means most of the traditional student performance prediction models have difficulty in dealing with multi-dimensional academic data, can cause sub-optimal classification and generate a simple generalized insight. To address these challenges of the existing system, in this research we propose a new model Multi-dimensional Student Performance Prediction Model (MSPP) that is inspired by advanced data preprocessing and feature engineering techniques using deep learning.

Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach.

The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.

Association of neighborhood-level socioeconomic disadvantage and Life's Essential 8 in early pregnancy.

We examined whether neighborhood-level socioeconomic disadvantage per the Area Deprivation Index (ADI) was associated with maternal cardiovascular health (CVH) in early pregnancy per the American Heart Association Life's Essential 8 (LE8). This is a cross-sectional analysis from the prospective Nulliparous Pregnancy Outcomes Study-Monitoring Mothers-to-Be Heart Health Study (nuMoM2b-HHS) cohort. The exposure was the ADI in tertiles (T) from least (T1) to most (T3) socioeconomic disadvantage.

Hypertensive disorders of pregnancy and gestational diabetes mellitus and predicted risk of maternal cardiovascular disease 10-14 years after delivery: A prospective cohort.

Aims: Studies evaluating the relationship between adverse pregnancy outcomes (APOs), namely hypertensive disorders of pregnancy (HDP) and gestational diabetes mellitus (GDM), with the estimated risk of atherosclerotic cardiovascular disease (ASCVD) remains limited and could inform patient-centred decision-making in the postpartum period. We examined whether HDP or GDM were associated with a higher 10- and 30-year predicted risk of ASCVD measured 10-14 years after delivery.

Methods: A secondary analysis from the international prospective Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (2013-2016) cohort.

Early detection of autism spectrum disorder: gait deviations and machine learning.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder diagnosed by clinicians and experts through questionnaires, observations, and interviews. Current diagnostic practices focus on social and communication impairments, which often emerge later in life. This delay in detection results in missed opportunities for early intervention.