Clinical decisions are more promising and evidence-based, hence, big data analytics to
assist clinical decision-making has been expressed for a variety of clinical fields. Due to the …

Heart disease, one of the main reasons behind the high mortality rate around the world,
requires a sophisticated and expensive diagnosis process. In the recent past, much …

Background: Machine Learning (ML) is becoming increasingly popular in healthcare,
particularly for improving the timing and accuracy of diagnosis. ML can provide disease …

Heart failure (HF) has been considered as one of the deadliest human diseases worldwide
and the accurate prediction of HF risks would be vital for HF prevention and treatment. To …

Background and objective Datamining (DM) has, over the last decade, received increased
attention in the medical domain and has been widely used to analyze medical datasets in …

The use of medical datasets has attracted the attention of researchers worldwide. Data
mining techniques have been widely used in develo** decision support systems for …

Enormous data growth in multiple domains has posed a great challenge for data processing
and analysis techniques. In particular, the traditional record maintenance strategy has been …

Vast amount of data available in health care industry is difficult to handle, hence mining is
necessary to find the necessary pattern and relationship among the features available …

With the arrival of Industry 4.0, intelligent condition-based maintenance has become a must,
if not a need, for industries with significant capital investments in rotating machineries. Tool …

Machine learning has emerged as an effective medical diagnostic support system. In a
medical diagnosis problem, a set of features that are representative of all the variations of …