Breakthrough in AI-Powered Insurance Fraud Detection: New Study Shows Hybrid Models Cut Losses and Boost Accuracy

In an era where insurance fraud drains billions from the global economy annually, a groundbreaking study by researchers ...
Frontiers

Machine learning-based prediction of hypoglycemia severity in hospitalized diabetic patients

Department of General Practice, The Affiliated Hospital of Qingdao University, Qingdao, China Objective: To identify risk factors for hypoglycemia in hospitalized patients with type 2 diabetes ...
Scientific Research Publishing

Joshi, R.D. and Dhakal, C.K. (2021) Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches. International Journal of Environmental Research and ...

ABSTRACT: This paper aims to investigate the effectiveness of logistic regression and discriminant analysis in predicting diabetes in patients using a diabetes dataset. Additionally, the paper ...
Frontiers

Exploring the association between vitamin D levels and dyslipidemia risk: insights from machine learning and generalized additive models

Introduction: Vitamin D is a necessary nutrient that is important for calcium homeostasis and bone health. Dyslipidemia is thought to be a risk factor for the development of atherosclerotic illnesses.
Live Science

Scientists use quantum machine learning to create semiconductors for the first time – and it could transform how chips are made

Quantum machine learning is a hybrid approach that combines classical data with quantum computing methods. In classical computing, data is stored in bits encoded as a 0 or 1. Quantum computers use ...
IEEE

Machine Learning–Based Hypertension Risk Stratification Using Logistic Regression and XGBoost

Abstract: Hypertension is a critical global health concern, necessitating accurate prediction models and effective prescription decisions to mitigate its risks. This study proposes a hybrid machine ...
Hosted on MSN

Logistic Regression Machine Learning Example ¦ Simply Explained

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...