Survival prediction model with lion optimization for Hepatitis-B patients using supervised and unsupervised learning algorithm

Abstract

The disease cancer takes millions of lives every year, although not being a contagious disease, the risk imposed by the cancer stands tall when compared to other diseases. Among those, liver cancer is one of the most commonly occurring cancers (841,080 cases diagnosed in 2018, which is 5 % of cancers that year). (HCC) is the of frequent source of liver cancer, which is the fastest improving causes of cancer death and is the second frequent occurring cancer of all. Hepatitis-B infection when moved into chronic stage causes to form cancer in the liver (HCC). Apparently 1 in 12 deaths in the world are due to liver cancer, in that 75 % of all liver cancers are caused by HCC, of which hepatitis-B virus reports 50 % cases HCC worldwide. To prognosis of survival rate using a dataset that contains lots of information or parameters about serum hepatitis patients. Initially the data will be pre-processed, to make better suitable for further processing and for being in acceptable format for the algorithms. Then, apply couple of algorithms to specify the prediction and bring out the accuracy of the model. And further compare those algorithms to specify the algorithm with most efficacy. The accuracy is calculated by comparing the predicted outcome with real-time outcome (i.e. “Class” = Live/Die) of the patient. Based on considering various parameters, the model will be predicting the risk of a patient of his survival rate.