AN EFFICIENT CLASSIFICATION ALGORITHM FOR LUNG CANCER PREDICTION USING BIG DATA ANALYTICS

The Cancer is a key disease that has become the greatest risk to public health cause to its complicated early recognition. According to a study by the WHO in 2019 and so far, there are four million new cases of cancer and 28.69 million cancer deaths worldwide. The world's most dangerous cancer is covid-19 implanted the lung cancer. The Lung cancer remainder the foremost source of cancer-related deaths in mutually men and women, and its frequency is growing worldwide. Lung cancer is the unrestrained augmentation of irregular cells that start in one or both lungs. Early recognition of Covid-19 is not an easier development to analyze lung cancer patents and confront the public with much pain. In this sense, contribute to the categorization of algorithms such as Naive Bayes, J48, Random Forest and AdaBoost to classify the detection of early lung cancer. This leads to better embattled and adapted healthcare solutions on behalf of cancer patients. The victorious accomplishment of the human genome project has led public to understand that inherited, environmental, and being factors must be collective to study cancer because of its complication. The main objective of this document is to make available a prior advice to users and an analysis of the piece of taxonomy algorithms.