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Tree-Based Learning Algorithms to Classify ECG with Arrhythmias

Sun et al. | Apr 23, 2025

Tree-Based Learning Algorithms to Classify ECG with Arrhythmias

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.

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Analyzing relationships and distribution between age, sex, and eye disease at IGMCH eye OPD

Sareen et al. | Feb 20, 2025

Analyzing relationships and distribution between age, sex, and eye disease at IGMCH eye OPD
Image credit: Amanda Dalbjörn

This study analyzed patient demographics in the ophthalmology department at Indira Gandhi Medical College and Hospital (IGMCH) to assess relationships between age, sex, and eye conditions. While the overall sex distribution was equal, individual conditions varied, with cataracts and retinal disorders more common in males and conjunctival conditions slightly more prevalent in females, though none were statistically significant (p > 0.05) except for cataract patients aged 50–89 (p < 0.001). Understanding these trends can help medical facilities allocate resources more effectively for improved patient care.

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Adults’ attitudes toward non-alcoholic beer purchases and consumption by children and adolescents

An et al. | Aug 23, 2024

Adults’ attitudes toward non-alcoholic beer purchases and consumption by children and adolescents
Image credit: The authors

Consumption of non-alcoholic beverages, like non-alcoholic beer, is growing in popularity in the United States. These beverages raise important societal questions, such as whether minors should be allowed to purchase or consume non-alcoholic beer. An and An investigate this issue by surveying adults to see if they support minors purchasing and consuming non-alcoholic beer.

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A HOG feature extraction and CNN approach to Parkinson’s spiral drawing diagnosis

Tripathi et al. | Aug 09, 2024

A HOG feature extraction and CNN approach to Parkinson’s spiral drawing diagnosis

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.

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