Query: diagnosis of lung cancer prediction using machine learning

title: Lung Cancer Prediction using Machine Learning: A Comprehensive Approach
abstract: The prominent cause of cancer related mortality throughout the globe is "Lung Cancer" Hence beforehand detection, prediction and diagnosis of lung cancer has become essential as it expedites and simplifies the consequent clinical board. To erect the progress and medication of cancerous conditions machine learning techniques have been utilized because of its accurate outcomes. Various types of machine learning algorithms(ML) like Naive Bayes, Support Vector Machine (SVM) Logistic regression, Artificial Neural Network (ANN) have been applied in the healthcare sector for analysis and prognosis of lung cancer. In this review, factors that cause lung cancer and application of ML algorithms are discussed up to date and also draws special attention to their relative strengths and weaknesses. This paper will help the researchers to quickly go through the related literature instead of referring to the many papers.

title: Lung Cancer Prediction Using Machine Learning Algorithms on Big Data: Survey
abstract: Lung cancer is a malignant lung tumour that is characterised by the regulated growth of cells in the lung tissue. The most common cancer diagnosed worldwide is lung cancer. More deaths than any other kind of cancer occur due to lung cancer. Early diagnosis and care are very useful and efficient for the survival of cancer patients. Different image processing and soft computing methods may be used for identifying cancer cells from medical images. Classification depends on features extracted from the images. In order to produce better classification results, the focus is on the feature extraction level. In order to distinguish a pattern that can provide some useful insights into what combination of features is most likely to result in an abnormality, this knowledge is then given to machine learning algorithms. 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.

title: Early Stage Lung Cancer Prediction Using Various Machine Learning Techniques
abstract: Lung cancer is one of the most common and serious diseases present around the world, which is observed in people of all age groups ranging from children to old people. Annually it costs a lot of money for the cure and diagnosis of people with lung cancer. The existing clinical techniques such as X Ray and other imaging procedures require complex hardware and considerable expense. Thus the most important issue is the prediction to be accurate and to use a reliable method for that. This raises the need for (comparatively more effective and cheaper) machine learning models in medical diagnosis using medical data sets. Long term tobacco smoking results in 85 percent of cases of lung cancer. About 10 15 percent of cases arise in people who never smoked. There are numerous methods and tools that are available now for data analysis and its computation. These technological advancements will be referred and used to develop prediction models in the project to predict the presence of lung cancer at an early stage in a patient.The study involves comparing various classification and ensemble models such as Support Vector Machine(SVM) K Nearest Neighbor (KNN) Random Forest(RF) Artificial Neural Networks (ANN) and a hybrid model, Voting classifier. The performance of the various models is compared and evaluated in terms of their accuracy. Thus, it is easy to identify a patient with lung cancer at an early stage using various sophisticated technologies of today.

title: 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.

title: Application of Machine Learning for Prediction of Lung Cancer using Omics Data
abstract: Cancer is one of the deadly diseases across many countries. However, cancer can be cured, if detected at an early stage. Researchers are working on healthcare for early detection and prevention of cancer. Medical data has reached its utmost potential by providing researchers with huge data sets collected from all over the globe. In the present scenario, Machine Learning has been widely used in the area of cancer diagnosis and prognosis. Survival analysis may help in the prediction of the early onset of disease, relapse, re occurrence of diseases and biomarker identification. Applications of machine learning and data mining methods in medical field are currently the most widespread in cancer detection and survival analysis. In this survey, different ways to detect and predict lung cancer using latest Machine learning algorithms combined with data mining has been analyzed. Comparative study of various machine learning techniques and technologies has been done over different types of data such as clinical data, omics data, image data etc.

title: Prediction of pathologic stage in non small cell lung cancer using machine learning algorithm based on CT image feature analysis
abstract: PurposeTo explore imaging biomarkers that can be used for diagnosis and prediction of pathologic stage in non small cell lung cancer (NSCLC) using multiple machine learning algorithms based on CT image feature analysis.MethodsPatients with stage IA to IV NSCLC were included, and the whole dataset was divided into training and testing sets and an external validation set. To tackle imbalanced datasets in NSCLC, we generated a new dataset and achieved equilibrium of class distribution by using SMOTE algorithm. The datasets were randomly split up into a training/testing set. We calculated the importance value of CT image features by means of mean decrease gini impurity generated by random forest algorithm and selected optimal features according to feature importance (mean decrease gini impurity 0.005) The performance of prediction model in training and testing sets were evaluated from the perspectives of classification accuracy, average precision (AP) score and precision recall curve. The predictive accuracy of the model was externally validated using lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples from TCGA database.ResultsThe prediction model that incorporated nine image features exhibited a high classification accuracy, precision and recall scores in the training and testing sets. In the external validation, the predictive accuracy of the model in LUAD outperformed that in LUSC.ConclusionsThe pathologic stage of patients with NSCLC can be accurately predicted based on CT image features, especially for LUAD. Our findings extend the application of machine learning algorithms in CT image feature prediction for pathologic staging and identify potential imaging biomarkers that can be used for diagnosis of pathologic stage in NSCLC patients.

title: The Diagnosis of Lung Cancer using Machine Learning
abstract: Nowadays cancer has become huge threat in human life there are many types of cancer, Lung cancer is one of the common types causing very high mortality rate. The best way of protection from lung cancer is its early detection and prediction The detection of lung cancer in early stage is a challenging problem, due to the structure of the cancer cells, where utmost of the cells are overlapped with each other.It is a computational procedure that sort images into groups according to their similarities.In this Histogram Equalization is used for preprocessing of the images and feature extraction process and Support Vector Machine classifier to check the condition of a patient in its early stage whether it is normal or abnormal. The performance is based on the correct and incorrect classification of the classifier.

title: A Comparative Study of Lung Cancer Detection using Machine Learning Algorithms
abstract: The growth of cancerous cells in lungs is called lung cancer. The mortality rate of both men and women has expanded due to the increasing rate of incidence of cancer. Lung cancer is a disease where cells in the lungs multiply uncontrollably. Lung cancer cannot be prevented but its risk can be reduced. So detection of lung cancer at the earliest is crucial for the survival rate of patients. The number of chain smokers is directly proportional to the number of people affected with lung cancer. The lung cancer prediction was analysed using classification algorithms such as Naive Bayes, SVM, Decision tree and Logistic Regression. The keyobjective of this paper is the early diagnosis of lung cancer by examining the performance of classification algorithms.

title: Optimization for Lung Cancer Prediction Using Support Vector Machine with Artificial Neural Networks A Review
abstract: Lung cancer is not an accidental death nowadays. After undergoing many researches, it is observed that death rate is increased. So, to reduce the death rate, people should go through some quicker diagnosis. Generally, lung cancer detection is done by using many techniques of different domains. Currently, there are many algorithms in use to detect the lung cancer for feature extraction and selection in the domain of machine learning. For segmentation purpose, super pixel segmentation is highly used. In this paper, we are going to propose the algorithm that is used to show accurate results in the detection of lung cancer. To find a algorithm or optimizing the existing algorithms like genetic optimization algorithms, PSO, SVM and comparing these algorithms with the existing ones then finalizing the standard algorithm that gives accuracy in formation of lung cancer detection system.

title: Early Detection of Depression and Treatment Response Prediction using Machine Learning: A Review
abstract: The turn of the century saw tremendous development in using Machine Learning (ML) for medical diagnosis. Majority of the prediction models or the diagnostic models were based on imaging. These methods extensively rely on huge dataset to develop a deep learning model to effectively diagnose conditions like Alzheimer's, Arrythmia, Lung Cancer, Breast Cancer among others. Early detection of depression is a major hurdle in effective and timely treatment. This paper explores the existing corpora of work on application of ML for early detection of depression. Further, a review on treatment response prediction is also elucidated. Relatively, the apparent dearth of work in depression has been found to be due to the scanty imaging dataset available on depression. We have reviewed the existing body of work on depression that have used imaging biomarkers, mobility traces, multi modal fusion of audio, video and text. Our hope in writing this paper is to spur and catalyze research in early detection of depression and effective prediction of response to its treatment.