Open AccessDisease Prediction by Machine Learning Over Big Data Lung Cancer
Author(s) -
T. Shanmugapriya,
T. Meyyappan
Publication year - 2021
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit206669
Subject(s) - naive bayes classifier , lung cancer , random forest , support vector machine , machine learning , computer science , disease , cancer , artificial intelligence , data mining , medicine , oncology , pathology
Lung Cancer is one of the deadly diseases in the world today. Lung Cancer is caused because of some genetic factors and/or environmental factors and/or today’s modern lifestyle. Lung cancer has become the primary reason of death in developed countries. The majority effective way to decrease lung cancer death is to detect it earlier. The in advance detection of cancer is not easier method but if it is detecte it is curable. Various works have been done in predicting lung cancer different data mining approach and algorithm were adopt by different people. All work has some limits such as lack of intelligent prediction, and incompetent in structure that forced to take up this problem and to implement the Data mining based cancer prediction System (DMBCPS). This has proposed the Lung cancer prediction system based on data mining. This system is validated by comparing its predicted results with patient’s prior medical information and it was analyzed by using weka tool system. We analyzed the lung cancer prediction using classification algorithm such as Naive Bayes, SVM and Random forest algorithm. The dataset have 782 instances and 31 attributes. The main aim of this paper is to provide the earlier warning to the users and the performance analysis of the classification algorithms.