Background Oral cancer can show heterogenous patterns of behavior. For proper and
effective management of oral cancer, early diagnosis and accurate prediction of prognosis …

AIM: The aim of this paper is to provide an overview of artifi cial neural network (ANN) in
biomedical domain and compare it with the logistic regression model. METHODS: Artifi cial …

Deep learning takes advantage of large datasets and computationally efficient training
algorithms to outperform other approaches at various machine learning tasks. However …

Objective To compare performance of risk prediction models for forecasting postoperative
sepsis and acute kidney injury. Design Retrospective single center cohort study of adult …

Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma
(OTSCC) remains a challenge in the field of head and neck oncology. We examined the use …

Experimental datasets in bioengineering are commonly limited in size, thus rendering
Machine Learning (ML) impractical for predictive modelling. Novel techniques of multiple …

For patients with locally advanced rectal cancer (LARC), achieving a pathological complete
response (pCR) after neoadjuvant chemoradiotherapy (CRT) provides them with the optimal …

Electronic Health Records are mainly designed to record relevant patient information during
their stay in the hospital for administrative purposes. They additionally provide an efficient …

The application of deep machine learning, a subfield of artificial intelligence, has become a
growing area of interest in predictive medicine in recent years. The deep machine learning …

Objectives To compare two statistical models, namely logistic regression and artificial neural
network (ANN), in prediction of vestibular schwannoma (VS) recurrence. Methods Seven …