Efficient multi-level lung cancer prediction model using support vector machine classifier

Manju, B R; Athira, V; Rajendran, Athul

doi:10.1088/1757-899x/1012/1/012034

Cited by 26 publications

(6 citation statements)

References 30 publications

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“…They [ 11 ] investigated several different ways of measuring lung growth. There were several of these, including the application of artificial neural networks, image processing, linear dependency analysis (LDA), and the self-organizing map (SOM).…”

Section: Related Workmentioning

confidence: 99%

“…In addition to screening, direct sequencing can be used to uncover mutations that were missed during the screening process. A genetic mutation in the EGF receptor (EGFR) has been discovered and can be utilised to detect genetic mutations in lung cancer [10][11][12][13]. It has been demonstrated that the artificial neural network (ANN) and support vector machine (SVM) outperform their nonensemble counterparts [14].…”

Section: Introductionmentioning

confidence: 99%

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Lung Cancer Prediction from Text Datasets Using Machine Learning

Kumar¹,

Harish²,

Ravi

et al. 2022

BioMed Research International

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Lung cancer is the major cause of cancer-related death in this generation, and it is expected to remain so for the foreseeable future. It is feasible to treat lung cancer if the symptoms of the disease are detected early. It is possible to construct a sustainable prototype model for the treatment of lung cancer using the current developments in computational intelligence without negatively impacting the environment. Because it will reduce the number of resources squandered as well as the amount of work necessary to complete manual tasks, it will save both time and money. To optimise the process of detection from the lung cancer dataset, a machine learning model based on support vector machines (SVMs) was used. Using an SVM classifier, lung cancer patients are classified based on their symptoms at the same time as the Python programming language is utilised to further the model implementation. The effectiveness of our SVM model was evaluated in terms of several different criteria. Several cancer datasets from the University of California, Irvine, library were utilised to evaluate the evaluated model. As a result of the favourable findings of this research, smart cities will be able to deliver better healthcare to their citizens. Patients with lung cancer can obtain real-time treatment in a cost-effective manner with the least amount of effort and latency from any location and at any time. The proposed model was compared with the existing SVM and SMOTE methods. The proposed method gets a 98.8% of accuracy rate when comparing the existing methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lung Cancer Prediction from Text Datasets Using Machine Learning

Kumar¹,

Harish²,

Ravi

et al. 2022

BioMed Research International

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“…The proposed GA-NN with SVM method is compared with existing approaches as indicated in (4,6,10,14) respectively.…”

Section: Performance Comparison With Existing Methodsmentioning

confidence: 99%

“…As authors have chosen chest radiography as image modality, false-positive and false-negative are more when they are overlapped with normal anatomical structures, such as heart clavicle and ribs. Authors in (6) have discussed the lung cancer detection using SVM. Dataset consists of malignant, benign and pre-malignant images, where PCA has been used for feature selection strategies to cut down the dimensionality.…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer Detection by Multiple Feature Subset Extraction and Selection based on SVM-Weights and Genetic Algorithm-Neural Network

Ashwini¹,

Kurian²,

Chidanandamurthy³

et al. 2023

IJST

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Objectives: To develop an optimum hybrid approach for lung cancer detection by multiple feature subset extraction and selection based on SVM-weights and Genetic Algorithm (GA-NN) in order to improve the performance measures such as accuracy, sensitivity and specificity. Methods: Initially in preprocessing phase, Computed Tomography (CT) lung images are de-noised using median filter and enhanced using contrast stretching. In the next phase, candidate patch extraction is formed and Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) features are extracted. This is followed by feature selection using Genetic Algorithm-Neural Network (GA-NN) with SVM weights. Finally, images are classified as cancerous and non-cancerous using multiple classifiers (SVM and KNN). For this research work, CT lung images are collected form LIDC dataset. Around 500 images are used out of which 70% is used for training and 30% is used for testing. Findings: From simulation results and comparative analysis, it is observed that GANN with SVM weights result in better predictive performance metrics with notable improvements. The suggested feature subset reduction outperforms current techniques for detection of lung cancer in CT images. The proposed method has resulted in improved accuracy, specificity and sensitivity by 95.8%, 91.3% and 93.5% respectively which is higher than the existing approaches. Novelty: This work presents a novel approach to detect the lung cancer by multiple feature subset extraction and selection based on SVM-Weights and Genetic Algorithm -Neural Network (GA-NN) with improved accuracy, sensitivity and specificity.

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“…In (Manju et al, 2021;Tharwat, 2019), SVM is mainly used to optimally separate the parameters of the two classes through optimization. In (Ayer et al, 2010;Dreiseitl et al, 2002;Şamkar et al, 2016;Shipe et al, 2019), LR is used in the analysis of experimental data, meteorology, and medicine.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer

Ozcan

Aydin

2022

Asian Pac J Cancer Prev

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Objective: To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. Methods: The “College of Wisconsin Breast Cancer Dataset”, which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the dataset was preprocessed. Sensitivity, accuracy, and definiteness metrics were used to measure the success of the methods. Result: Compared to other ML algorithms used in the study, the GBC ML algorithm was found to be the most successful method in the classification of tumors with an accuracy of 99.12%. The XGB ML algorithm was found to be the lowest method with an accuracy rate of 88.10%. In addition, it was determined that the general accuracy rates of the 11 ML algorithms used in the study varied between 88-95%. Conclusion: When the results obtained from the ML classifiers used in the study are evaluated, the efficiency of the GBC algorithm in the classification of tumors is obvious. It can be said that the success rates obtained from 11 different ML algorithms used in the study are valuable in terms of being used to predict different cancer types.

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Efficient multi-level lung cancer prediction model using support vector machine classifier

Cited by 26 publications

References 30 publications

Lung Cancer Prediction from Text Datasets Using Machine Learning

Lung Cancer Prediction from Text Datasets Using Machine Learning

Lung Cancer Detection by Multiple Feature Subset Extraction and Selection based on SVM-Weights and Genetic Algorithm-Neural Network

Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer

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