Morning Overview on MSN

Many AI disease-risk models trained on flawed health data

Somewhere on Kaggle, the open data platform where anyone can upload a spreadsheet and call it a dataset, two files labeled as ...

Machine learning tool pinpoints optimal locations for tree planting, offering a powerful tool for climate mitigation

Afforestation—establishing forests on previously non-forested land, or where forests have not existed for a long time—is one ...
Oncology Nurse Advisor

Machine Learning Model Can Predict Hepatocellular Carcinoma Risk

Final random-forest-based models outperformed all publicly available risk scores on internal and external test sets.
IEEE

Random Forest Algorithm for the Prediction of Diabetes

Abstract: Diabetes is taken into account together of the deadliest and chronic disease that causes a rise in glucose. Polygenic disease is that the kind wherever the exocrine gland doesn't manufacture ...
Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ()

This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates Random Forest ...
The New York Times

48 Hours Without A.I.

It’s everywhere, as the author learned the hard way while making as little contact as possible with machine learning and generative artificial intelligence. It’s everywhere, as the author learned the ...
The American Journal of Managed Care

Novel Machine Learning Model Improves MASLD Detection in Type 2 Diabetes

MASLD is prevalent in T2DM patients, with a 65% occurrence rate, and poses a higher risk for severe liver diseases. The study analyzed 3,836 T2DM patients, identifying key predictors like BMI, ...
Frontiers

Evaluating machine learning models for stroke prediction based on clinical variables

Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as the Framingham ...
Frontiers

Postpartum depression risk prediction using explainable machine learning algorithms

Postpartum depression (PPD) is a common and serious mental health complication after childbirth, with potential negative consequences for both the mother and her infant. This study aimed to develop an ...