The number of patients of malignant soft tissue tumor is about 3,000 every year in Japan. In
the treatment procedure of soft tissue tumor, survivability of patient must be predicted based …

We utilize deep neural networks to develop prediction models for patient survival and
conditional survival of colon cancer. Our models are trained and validated on data obtained …

We propose a prognostic machine learning (ML) framework to support the behavioural
outcome prediction for cancer survivors. Specifically, our contributions are four-fold:(1) …

Lung cancer is one of the main leading causes of cancer death in all over the world.
Accurate prediction of lung cancer survivability can enable physicians to make more reliable …

This paper presents a classifier ensemble approach for predicting the survivability of the
breast cancer patients using the latest database version of the Surveillance, Epidemiology …

Background: As cancer is the leading global cause of death, an ongoing challenge is
predicting an individual's cancer progression accurately, to facilitate personalized treatment …

Lung cancer, one of the deadliest forms of cancer, can significantly improve patient survival
rates by 60–70% if detected in its early stages. The prediction of lung cancer patient survival …

Prediction in health care is closely related with the decision-making process. On the one
hand, accurate survivability prediction can help physicians decide between palliative care or …

Abstract The Healthcare Analytics (HcA) is a process in which clinical data is analyzed and
patient's treatment is performed. The treatment depends on the analysis of clinical data …

Cancer survivability or survival optimization models are crucial for treatment programs in
hospitals. Predictive models can enable primary care physicians in providing more informed …