Prediction of Lung Cancer Using Machine Learning Classifier

Abstract

Lung cancer generally occurs in both male and female due to uncontrollable growth of cells in the lungs. This causes a serious breathing problem in both inhale and exhale part of chest. Cigarette smoking and passive smoking are the principal contributor for the cause of lung cancer as per world health organization. The mortality rate due to lung cancer is increasing day by day in youths as well as in old persons as compared to other cancers. Even though the availability of high tech Medical facility for careful diagnosis and effective medical treatment, the mortality rate is not yet controlled up to a good extent. Therefore it is highly necessary to take early precautions at the initial stage such that it’s symptoms and effect can be found at early stage for better diagnosis. Machine learning now days has a great influence to health care sector because of its high computational capability for early prediction of the diseases with accurate data analysis. In our paper we have analyzed various machine learning classifiers techniques to classify available lung cancer data in UCI machine learning repository in to benign and malignant. The input data is prepossessed and converted in to binary form followed by use of some well known classifier technique in Weka tool to classify the data set in to cancerous and non cancerous. The comparison technique reveals that the proposed RBF classifier has resulted with a great accuracy of 81.25% and considered as the effective classifier technique for Lung cancer data prediction.

Parallel extreme gradient boosting classifier for lung cancer detection

“…Also, Li et al [19] proposed a fusion algorithm that combines handcrafted features into the features learned at the output layer of a 3D deep convolutional neural network (CNN). Patra [20] analyzed various machine learning classifier techniques to classify lung cancer into benign and malignant. Lai et al [21] trained clinical and gene expression data with improved deep neural network (DNN).…”

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

“…In their research, they observed that LR with a 7-fold cross validation regime and SVM with a 10-fold cross validation strategy outperformed the other classifiers on the UCI repository and SLCD dataset respectively. Recently, Patra [161] compared the performances of four different classical ML algorithms: RBF-NN, k-NN, NB and J48 on the Lung Cancer dataset of the UCI repository. The experimental outcomes confirmed that NN produces the best accuracy with a 10-fold cross validation technique.…”

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“…Furthermore, since interpretation is highly reliant on prior experience, less trained radiologists have significant variability in detecting subtle lung cancers. For the detection of lung nodules, there has been significant variation in performance amongst radiologists.CT scan analysis is further complicated by the complex airway and vessel layout [12].…”