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Commercial Concentrated Animal Feeding Operations (CAFOs) produce large quantities of waste material from the animals being housed in them. These feedlots found across the United States contain livestock that produce waste that results in hazardous runoff. This study examines how CAFOs affect water sources by testing for Escherichia Coli (E. coli) content in bodies of water near CAFOs.

Impact of carbon tax and dividend on financial security

The authors compare current machine learning algorithms with a new Explainable AI algorithm that produces a human-comprehensible decision tree alongside predictions.

Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.

Arrhythmias vary in type and treatment, and ECGs are used to detect them, though human interpretation can be inconsistent. The researchers tested four tree-based algorithms (gradient boosting, random forest, decision tree, and extra trees) on ECG data from over 10,000 patients.

The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.

The authors survey high school students regarding preferences for taking notes by hand versus typing.

The authors looked at the ability to detect heart disease before the onset of severe clinical symptoms.

The authors looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.

Autism spectrum disorder (ASD) is hard to correctly diagnose due to the very subjective nature of diagnosing it: behavior analysis. Due to this issue, we sought to find a machine learning-based method that diagnoses ASD without behavior analysis or helps reduce misdiagnosis.