AIMC Topic: Decision Trees

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Machine learning models in predicting viability after testicular torsion: a proof of concept study.

Pediatric surgery international
PURPOSE: Decision-making for orchiectomy following testicular torsion often relies on subjective clinical evaluations. This study investigates the efficacy of machine learning (ML) models in objectively predicting post-torsion testicular viability, a...

Selective classification with machine learning uncertainty estimates improves ACS prediction: a retrospective study in the prehospital setting.

Scientific reports
Accurate identification of acute coronary syndrome (ACS) in the prehospital setting is important for timely treatments that reduce damage to the compromised myocardium. Current machine learning approaches lack sufficient performance to safely rule-in...

Intrusion detection using search-based learning optimized ensemble tree classifier model.

PloS one
An Intrusion Detection System (IDS) is an important component of cybersecurity, meant to monitor malicious behaviour, detect, and respond to unauthorized activities in computer systems or networks. Generally, Intrusion detection (IDS) is classified i...

Enhanced machine learning and hybrid ensemble approaches for Coronary Heart Disease prediction.

PloS one
Coronary heart disease (CHD) remains the leading cause of mortality worldwide, disproportionately affecting low- and middle-income countries where diagnostic resources are limited. Traditional statistical models often fail to deliver adequate predict...

Health-related quality of life among healthcare workers: a comparative analysis using regression, conditional tree and forests.

BMC public health
BACKGROUND: Considering the potential importance of health care workers (HCWs) in maintaining and improving the health of society, we decided to investigate the factors affecting the health-related quality of life (HRQoL) of HCWs using machine learni...

Development and validation of a screening model for early diagnosis of biliary atresia in neonates with cholestasis.

Pediatric surgery international
BACKGROUND: Biliary atresia (BA) is a progressive neonatal cholestatic liver disease that requires timely diagnosis and intervention. Differentiating BA from other causes of neonatal cholestasis remains a significant clinical challenge.

TC check: a web app for thyroid cancer recurrence prediction using explainable machine learning.

Journal of cancer research and clinical oncology
BACKGROUND: Thyroid cancer (TC) is one of the most prevalent endocrine malignancies, and its recurrence presents a major clinical challenge that can adversely affect patient prognosis and treatment outcomes. Despite the progress in diagnostic methods...

An Interpretable Hybrid AI Model for Breast Fine Needle Aspiration Cytology Image Classification.

Journal of medical systems
While Fine needle aspiration cytology (FNAC) and mammography are both used to diagnose breast lesions, FNAC is generally more accurate than mammograms for predicting breast cancer. It is also gaining popularity as an early detection tool due to its r...

An approach to make general practitioner referrals suitable for artificial intelligence deployment.

The New Zealand medical journal
Outpatient referrals for hospital specialist assessment are an increasing workload that carry significant risk if not attended to in a timely manner. This viewpoint discusses how decision support (including artificial intelligence and machine learnin...

Improved predictive formulae for wave overtopping at sloped breakwaters using interpretable machine learning models.

PloS one
Accurate prediction of mean wave overtopping discharge is essential for the safe and cost-effective design of coastal defence structures. While traditional empirical, physical, and numerical models remain important, Machine Learning (ML) has recently...