Background and purpose Machine learning (ML) has attracted much attention with the hope
that it could make use of large, routinely collected datasets and deliver accurate …

This paper presents a prototype to classify stroke that combines text mining tools and
machine learning algorithms. Machine learning can be portrayed as a significant tracker in …

Bayesian network is an increasingly popular method in modeling uncertain and complex
problems, because its interpretability is often more useful than plain prediction. To satisfy the …

The epidemiological question of concern here is “can young children at risk of obesity be
identified from their early growth records?” Pilot work using logistic regression to predict …

Background: Today there are abounding collected data in cases of various diseases in
medical sciences. Physicians can access new findings about diseases and procedures in …

Ever since the first cities were created, they have been dependent on technology to sustain
life. The smart city paradigm integrates advanced monitoring, sensing, communication, and …

This paper presents work on using data extracted from Twitter to predict the outcome of the
latest USA presidential elections on 8th of November 2016 in three key states: Florida, Ohio …

Objective: To develop a decision support system (DSS) for the prediction of the
postoperative outcome of a kidney stone treatment procedure, particularly percutaneous …

Stroke is a neurological disease that occurs when a brain cells die as a result of oxygen and
nutrient deficiency. Stroke detection within the first few hours improves the chances to …

This paper provides an analysis and proposes a methodology for predicting traffic
congestion. Several machine learning algorithms and approaches are compared to select …