New algorithm identifies disease-linked changes in cells without prior training

A new algorithm could drive breakthroughs in understanding cancer, Alzheimer's disease and other potentially fatal conditions ...
AHA/ASA Journals

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...
Securities.io

Crypto Security Needs Market-Aware Phishing Detection

In the modern age where most assets and valuable information are digitalized, phishing scams are an important way criminals steal money, much higher than “normal” physical robbery & extortion, with up ...
The Banker

Banks and regulators clash over ‘black box’ AI credit risk

Earlier this year when a UK Treasury Committee released a report warning that regulators’ complacency on AI in financial ...
Phys.org

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 of the nature-based and cost-effective solutions for climate change ...
Nature

Interpretable machine learning based decision tree model for predicting obstructive airway disease in a large non-smoking health screening population

Obstructive airway disease is defined by reduced FEV₁ and an FEV₁/FVC ratio below 70%. While pulmonary function testing is essential, few studies have used demographic, biochemical, and lifestyle data ...
Visual Studio Magazine

Decision Tree Regression from Scratch Without Pointers or Recursion Using C#

A decision tree regression system incorporates a set of if-then rules to predict a single numeric value. Decision tree regression is rarely used by itself because it overfits the training data, and so ...
Frontiers

Clinical decision system for renal cell carcinoma integrating interpretable machine learning algorithms

Kidney cancer is a highly heterogeneous oncologic disease with historically poor prognosis. Precise assessment of the risk of distal metastasis can facilitate risk stratification and improve prognosis ...
Frontiers

Salp swarm-optimized machine learning models for predicting preoperative aortic rupture risk in acute type a aortic dissection patients

Acute Type A aortic dissection (ATAAD) is characterized by acute onset and rapid progression, with aortic rupture due to dissection extension being the primary lethal mechanism. Timely identification ...
Nature

Decision tree-based machine learning methods for identifying colorectal cancer-associated microRNA signatures and their regulatory networks

This study aimed to identify candidate diagnostic miRNAs from the serum of colorectal cancer (CRC) patients using Boruta, a wrapper-based feature selection technique, in combination with decision tree ...
ahajournals.org

The Promise and Peril of Learning Algorithms and Predictive Models in Clinical Decision Support: Be Careful What You Train For

The era of predictive modeling enhanced with machine learning and artificial intelligence (AI) to aid clinical decision-making through the identification and ...