A Unified Hybrid Model for Cardiovascular Risk Prediction: Merging Statistical, Kernel-Based and Neural Approaches.

Cardiovascular diseases (CVDs) are still the leading cause of death in the worldwide. Traditional machine learning models often have difficulty in determine how to capture the complex links between disease risk factors and disease occurrence. This article discusses a hybrid machine learning approach for cardiovascular risk prediction (HMLCRP) to address this problem.

Advancing blood cell detection and classification: performance evaluation of modern deep learning models.

The detection and classification of blood cells are important in diagnosing and monitoring a variety of blood-related illnesses, such as anemia, leukemia, and infection, all of which may cause significant mortality. Accurate blood cell identification has a high clinical relevance in these patients because this would help to prevent false-negative diagnosis and to treat them in a timely and effective manner, thus reducing their clinical impacts.Our research aims to automate the process and eliminate manual efforts in blood cell counting.

Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging.

Introduction: Combining many types of imaging data-especially structural MRI (sMRI) and functional MRI (fMRI)-may greatly assist in the diagnosis and treatment of brain disorders like Alzheimer's. Current approaches are less helpful for forecasting, however, as they do not always blend spatial and temporal patterns from different sources properly. This work presents a novel mixed deep learning (DL) method combining data from many sources using CNN, GRU, and attention techniques.

Integrative approach for efficient detection of kidney stones based on improved deep neural network architecture.

In today's digital world, with growing population and increasing pollution, unhealthy lifestyle habits like irregular eating, junk food consumption, and lack of exercise are becoming more common, leading to various health problems, including kidney issues. These factors directly affect human kidney health. To address this, we require early detection techniques that rely on text data.