May 15, 2018 · In this article, we provide an overview of the main lung cancer prediction approaches proposed to date and highlight some of their relative strengths and weaknesses. We discuss some of the challenges in the development and validation of such techniques and outline the path to clinical adoption.

This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions.

Sep 1, 2021 · This article aims to hybridize the classification algorithms, including Support Vector Machine (SVM) and Neural network (NN) by using both the mechanism of NN, namely the Feed Forward (FF) and the Back Propagation (BP).

Jun 7, 2023 · Various machine learning-based methods, such as SVM and logistic regression, are used to predict lung cancer in this paper. The author has chosen only ten important features in the dataset. It is important to measure the performance using these mentioned algorithms.

Oct 1, 2023 · In this paper, we review and explore studies using machine learning to predict lung cancer survival based on clinical data. It provides an overview of data sources, patient cohort, data pre-processing, feature selection algorithms, selected features, ML …

May 10, 2023 · In this research paper, several models are experimented by using chest X-ray image or CT scan as an input to detect a particular disease. This research work is carried out to identify the best performing deep learning techniques for lung disease prediction.

This project predicts lung cancer using machine learning models: SVM, Logistic Regression, KNN, AdaBoost, and Random Forest. It includes data preprocessing, model training, evaluation, and comparative analysis of model accuracy.

Mar 15, 2022 · The cancer infected and non-infected lung’s CT scan images can be distinguished and separated by the use of a machine learning technique, namely support vector machine (SVM). In this proposed technique, it gives the best results compared to …

The prediction of lung cancer is analysed using various machine learning classification algorithms such as Naive Bayes, Support Vector Machine, Artificial Neural Network and Logistic Regression. The key aim of this paper is to diagnose lung cancer early by examining the performance of exist classification algorithms.

Mar 12, 2025 · This study emphasizes the usage of machine learning models like SVM in clinical practice to improve the detection rate of early lung cancer as a binary classification task.