Open AccessEfficient multi-level lung cancer prediction model using support vector machine classifier
Author(s) -
B R Manju,
V Athira,
Athul Rajendran
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1012/1/012034
Subject(s) - support vector machine , confusion matrix , principal component analysis , computer science , classifier (uml) , artificial intelligence , relevance vector machine , machine learning , confusion , structured support vector machine , data mining , pattern recognition (psychology) , psychology , psychoanalysis
This paper aims at the requirement for an interactive learning framework which empowers the successful checking of disorder in a patient. Principal component analysis stands out as an outstanding algorithm to significantly classify the target classes. PCA blends associated characteristics and makes a dissipated showcase of its components well. Scree plot examination gives solidarity of how many principal components are to be retained. Support Vector Machines (SVM ) is a fast and dependable classification algorithm that outperforms other techniques with a limited amount of data. The obtained components will be served to Support Vector Machine for further classification. The pre-dangerous stage will remind the clinical experts to give additional consideration to those patients. The expectation ability is estimated in terms of the confusion matrix. The model developed gives a high and uncompromising accuracy in early detection of different levels of malignancy