Predicting postoperative survival of lung cancer patients (LCPs) is an important problem of
medical decision‐making. However, the imbalanced distribution of patient survival in the …

There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop
an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This …

Medical outcomes must be tracked in order to enhance quality initiatives, healthcare
management, and mass education. Thoracic surgery data have been acquired for those who …

Purpose: This study aims to investigate the potential to improve lung cancer recurrence risk
prediction performance for stage I NSCLS patients by integrating oversampling, feature …

Prediction of survival expectancy after surgery is so important. Soft computing approaches
using training data are good approximations to model the different systems. We present …

One of the most common and leading cause of cancer death in human beings is lung
cancer. The advanced observation of cancer takes the main role to inflate a patient's …

Cardiovascular disease is a substantial cause of mortality and morbidity in the world. In
clinical data analytics, it is a great challenge to predict heart disease survivor. Data mining …

Background The current research aimed to develop an artificial intelligence predictive
system for individual survival rate of lung adenocarcinoma (LUAD). Methods Independent …

Cancer is a threatening disease for the human race affecting most people around the world.
The topmost reason for cancer demise across the globe is lung cancer and therefore there …

Dealing with multiclass classification problem is still considered as significant hurdle to
determine an efficient classifier. Moreover, this task is getting rough when it comes to …