Download or read book Optimized Feature Selection for Enhancing Lung Cancer Prediction Using Machine Learning Techniques written by Shanthi S and published by Ary Publisher. This book was released on 2023-02-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lung cancer is a major cause of cancer-related deaths worldwide. Machine learning techniques have shown promising results in the early detection and prediction of lung cancer. However, high-dimensional data, such as gene expression profiles, can introduce noise and decrease the classification accuracy of machine learning models. Feature selection techniques can alleviate this issue by identifying the most relevant and informative features, leading to better model performance. Optimized feature selection techniques can enhance the prediction accuracy of lung cancer using machine learning algorithms. Support vector machines, random forest, and artificial neural networks are commonly used algorithms for lung cancer prediction. By optimizing feature selection, these models can be trained with the most informative features, reducing overfitting and improving classification accuracy. Cross-validation techniques can also be used to evaluate the performance of feature selection and machine learning algorithms. The integration of optimized feature selection with machine learning techniques can provide an accurate and reliable lung cancer prediction model, which has the potential to improve early detection and precision medicine for lung cancer patients. Overall, optimized feature selection for enhancing lung cancer prediction using machine learning techniques is a promising approach to improving patient outcomes and reducing the global burden of lung cancer.

Download or read book Optimized Predictive Models in Health Care Using Machine Learning written by Sandeep Kumar and published by John Wiley & Sons. This book was released on 2024-03-06 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application; emphasizes validating and evaluating predictive models; provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare; highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.

Download or read book Cancer Prediction for Industrial IoT 4 0 written by Meenu Gupta and published by CRC Press. This book was released on 2021-12-31 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines. Features • Covers the fundamentals, history, reality and challenges of cancer • Presents concepts and analysis of different cancers in humans • Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer • Offers real-world examples of cancer prediction • Reviews strategies and tools used in cancer prediction • Explores the future prospects in cancer prediction and treatment Readers will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions. This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.

Download or read book Handbook of Machine Learning for Computational Optimization written by Vishal Jain and published by CRC Press. This book was released on 2021-11-02 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers.

Download or read book DATA SCIENCE WORKSHOP Lung Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-08-12 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Data Science Workshop presents a comprehensive journey through lung cancer analysis. Beginning with data exploration, the dataset is thoroughly examined to uncover insights into its structure and contents. The focus then shifts to categorizing features and understanding their distribution patterns, revealing key trends and relationships that could impact the predictive models. To predict lung cancer using machine learning models, an extensive grid search is conducted, fine-tuning model hyperparameters for optimal performance. The iterative process involves training various models, such as K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron, and evaluating their outcomes to select the best-performing approach. Utilizing GridSearchCV aids in systematically optimizing parameters to enhance predictive accuracy. Deep Learning is harnessed through Artificial Neural Networks (ANN), which involve building multi-layered models capable of learning intricate patterns from data. The ANN architecture, comprising input, hidden, and output layers, is designed to capture the complex relationships within the dataset. Metrics like accuracy, precision, recall, and F1-score are employed to comprehensively evaluate model performance. These metrics provide a holistic view of the model's ability to classify lung cancer cases accurately and minimize false positives or negatives. The Graphical User Interface (GUI) aspect of the project is developed using PyQt, enabling user-friendly interactions with the predictive models. The GUI design includes features such as radio buttons for selecting preprocessing options (Raw, Normalization, or Standardization), a combobox for choosing the ANN model type (e.g., CNN 1D), and buttons to initiate training and prediction. The PyQt interface enhances usability by allowing users to visualize predictions, classification reports, confusion matrices, and loss-accuracy plots. The GUI's functionality expands to encompass the entire workflow. It enables data preprocessing by loading and splitting the dataset into training and testing subsets. Users can then select machine learning or deep learning models for training. The trained models are saved for future use to avoid retraining. The interface also facilitates model evaluation, showcasing accuracy scores, classification reports detailing precision and recall, and visualizations depicting loss and accuracy trends over epochs. The project's educational value lies in its comprehensive approach, taking participants through every step of a data science pipeline. Attendees gain insights into data preprocessing, model selection, hyperparameter tuning, and performance evaluation. The integration of machine learning and deep learning methodologies, along with GUI development, provides a well-rounded understanding of creating predictive tools for real-world applications. Participants leave the workshop empowered with the skills to explore and analyze medical datasets, implement machine learning and deep learning models, and build user-friendly interfaces for effective interaction. The workshop bridges the gap between theoretical knowledge and practical implementation, fostering a deeper understanding of data-driven decision-making in the realm of medical diagnostics and classification.

Download or read book Innovations in Bio Inspired Computing and Applications written by Václav Snášel and published by Springer. This book was released on 2015-12-14 with total page 571 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Volume contains the papers presented during the 6th International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2015 which was held in Kochi, India during December 16-18, 2015. The 51 papers presented in this Volume were carefully reviewed and selected. The 6th International Conference IBICA 2015 has been organized to discuss the state-of-the-art as well as to address various issues in the growing research field of Bio-inspired Computing which is currently one of the most exciting research areas, and is continuously demonstrating exceptional strength in solving complex real life problems. The Volume will be a valuable reference to researchers, students and practitioners in the computational intelligence field..

Download or read book Improved Prediction of Gene Expression of Epigenomics Data of Lung Cancer Using Machine Learning and Deep Learning Models written by ZhengXin Shi and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Epigenetics is the study of biological mechanisms that will switch genes on and off, its alterations are deeply involved in the change of gene expression among various diseases including cancers. Machine learning is frequently used in cancer diagnosis and detection. In this research, four types of data are used towards the correct prediction of lung cancer, including DNA Methylation data, Histone data, Human Genome data, and RNA-Seq data. Four feature selection methods - ReliefF, Gain Ratio (GR), Principle Component Analysis (PCA), Correlation-based feature selection (CFS) and seven different classifiers - Random Forest (RF), Support Vector Machine (SVM) with Gaussian Kernel function and Linear Kernel function, Logistic Regression (LR), Naive Bayes (NB), Artificial Neural Network, and Convolutional Neural Network (CNN) were implemented in this study. The processing of these data sets is done using custom R-script. The tools that were used for feature selection and classification in the presented work are Weka 3 and Python. With the help of machine learning and deep learning methods, we were able to improve the accuracy and area under the curve (AUC) of the lung cancer prediction from an earlier published work. It was observed that the CNN model overperformed the other six classification methods.

Download or read book Deep Learning Techniques for Analyzing Clinical Lung Cancer Data written by Haoze Du and published by . This book was released on 2019 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the continued public concerns about cancer identification in patients, many methods have been implemented to analyze clinical records to gain actionable information and make a meaningful prediction of cancer patients outcomes. It is necessary to accurately predict the efficacy of specific therapy or identify a combination of actionable treatments on clinical practice based on clinical datasets. While conventional machine learning methods such as artificial neural networks and support vector machines have shown promise, they clearly have significant room for improvement. In this thesis, we attempted to train and optimize an innovative deep learning method called cascade forest, which is inspired by artificial neural networks, as well as a number of traditional machine learning methods and deep neural networks. Cutting edge machine learning tools such as Tensorflow and Scikit-learn on the GPU platform, which allows parallel computation to enhance their performances, were used to improve the time efficiency. The outcomes of this thesis include: 1) predicting the outcomes of a cancer patient based on clinical data from the publicly available SEER database; 2) evaluating the patient outcomes by comparing the models based on different datasets; 3) attempting to increase the accuracy and reduce the execution time for model training by optimizing machine learning models.

Download or read book Analysis Of Biological Data A Soft Computing Approach written by Sanghamitra Bandyopadhyay and published by World Scientific. This book was released on 2007-09-03 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, they are scattered in different journals, conference proceedings and technical reports, thus causing inconvenience to readers, students and researchers.This book, unique in its nature, is aimed at providing a treatise in a unified framework, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. Interesting research articles from eminent scientists around the world are brought together in a systematic way such that the reader will be able to understand the issues and challenges in this domain, the existing ways of tackling them, recent trends, and future directions. This book is the first of its kind to bring together two important research areas, soft computing and bioinformatics, in order to demonstrate how the tools and techniques in the former can be used for efficiently solving several problems in the latter.

Download or read book Machine Learning in Radiation Oncology written by Issam El Naqa and published by Springer. This book was released on 2015-06-19 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Download or read book Machine Learning and Deep Learning Techniques for Medical Image Recognition written by Ben Othman Soufiene and published by CRC Press. This book was released on 2023-12-01 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.

Download or read book Python Machine Learning written by Wei-Meng Lee and published by John Wiley & Sons. This book was released on 2019-04-04 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart—it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand. • Python data science—manipulating data and data visualization • Data cleansing • Understanding Machine learning algorithms • Supervised learning algorithms • Unsupervised learning algorithms • Deploying machine learning models Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level.

Download or read book A Genetic Algorithm Approach to Feature Selection for Computer Aided Detection of Lung Nodules written by Matthew J. Sprague and published by . This book was released on 2016 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lung cancer is responsible for the majority of cancer related deaths in the United States. One way to improve the chance of survival is early detection of Lung Nodules. Lung nodules are small, spherical, potentially cancerous growths within the lung. Several Computer Aided Detection (CAD) systems have been developed to aid in the detection of lung nodules both in computed tomography (CT) and chest radiograph scans. To increase performance and reduce the number of false positives, or misclassifications, in the detection, a feature selection technique is often applied to CAD systems. Feature selection is a method of selecting an optimal subset of features from all features calculated. In this case, a feature is defined as a quantitative characteristic calculated for a potential lung nodule directly from the input scan. Examples of simple features calculated for CAD systems include size, brightness, and shape of potential lung nodules. Common algorithms for feature selection include genetic algorithms and sequential forward selection. This paper proposes a genetic algorithm approach to feature selection for lung nodule CAD systems. Using existing CAD systems with our new feature selection technique, performance is evaluated on both CT scans using the LIDC-IDRI dataset as well as Chest Radiograph scans using the JRST dataset. A total number of 503 features are evaluated for the CT CAD system and 117 features for chest radiographs. Both classification systems utilize the Fisher Linear Discriminant (FLD) classifier. A composite GA fitness function is implemented capable of minimizing the number of false positives in addition to the size of the subset selected. Experimental results indicate that for CAD systems employing a high number of features, a genetic algorithm approach is superior compared to sequential forward selection in both Computed Tomography and Chest Radiography CAD systems.

Download or read book Characterizing Pulmonary Nodules Using Machine and Deep Learning Methods to Improve Lung Cancer Diagnosis written by Shiwen Shen and published by . This book was released on 2018 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Low-dose computed tomography (CT) screening has been widely used to detect and diagnose early stage lung cancer. Clinical trials have shown that low-dose CT reduced lung cancer mortality by 20% relative to plain chest radiography; however, challenges exist in current low-dose CT screening programs including high over-diagnosis rates, high cost and increased radiation exposure. This dissertation attempts to overcome these challenges by developing machine and deep learning models for automated lung cancer diagnosis and disease progression estimation. A novel lung segmentation approach was first developed using a bidirectional chain code method and machine learning framework. This method is designed to include the lung nodules attached to lung wall while minimizing over-segmentation error. Second, a hybrid ensemble convolutional neural network has been developed to classify lung nodule vs. non-nodule objects. The ensemble model combines the VGG, residual and densely connected module designs to improve the model classification robustness for external datasets collected with different acquisition parameters. Third, a hierarchical semantic convolutional neural network (HSCNN) has been described to classify lung nodule malignancy. Semantic characteristic features, predicted in parallel with the malignancy for each nodule, enable the interpretation of the model and improvement of malignancy prediction. Finally, a Bayesian framework combined with a continuous-time Markov model was developed to estimate the multi-state disease progression of lung cancer. The resulting model estimates individual lung cancer state transition information, providing the basis for personalized screening recommendations. Extensive experiments and results have proved the effectiveness of these methods paving the way to optimize and improve the effectiveness of existing low-dose CT screening programs.

Download or read book Data Mining in Bioinformatics written by Jason T. L. Wang and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.

Download or read book Artificial Intelligence Techniques for Advanced Computing Applications written by D. Jude Hemanth and published by Springer Nature. This book was released on 2020-07-23 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features a collection of high-quality research papers presented at the International Conference on Advanced Computing Technology (ICACT 2020), held at the SRM Institute of Science and Technology, Chennai, India, on 23–24 January 2020. It covers the areas of computational intelligence, artificial intelligence, machine learning, deep learning, big data, and applications of artificial intelligence in networking, IoT and bioinformatics

Download or read book The Illustrated Wavelet Transform Handbook written by Paul S. Addison and published by CRC Press. This book was released on 2017-01-06 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance has been fully updated and revised to reflect recent developments in the theory and practical applications of wavelet transform methods. The book is designed specifically for the applied reader in science, engineering, medicine and finance. Newcomers to the subject will find an accessible and clear account of the theory of continuous and discrete wavelet transforms, while readers already acquainted with wavelets can use the book to broaden their perspective. One of the many strengths of the book is its use of several hundred illustrations, some in colour, to convey key concepts and their varied practical uses. Chapters exploring these practical applications highlight both the similarities and differences in wavelet transform methods across different disciplines and also provide a comprehensive list of over 1000 references that will serve as a valuable resource for further study. Paul Addison is a Technical Fellow with Medtronic, a global medical technology company. Previously, he was co-founder and CEO of start-up company, CardioDigital Ltd (and later co-founded its US subsidiary, CardioDigital Inc) - a company concerned with the development of novel wavelet-based methods for biosignal analysis. He has a master’s degree in engineering and a PhD in fluid mechanics, both from the University of Glasgow, Scotland (founded 1451). His former academic life as a tenured professor of fluids engineering included the output of a large number of technical papers, covering many aspects of engineering and bioengineering, and two textbooks: Fractals and Chaos: An Illustrated Course and the first edition of The Illustrated Wavelet Transform Handbook. At the time of publication, the author has over 100 issued US patents concerning a wide range of medical device technologies, many of these concerning the wavelet transform analysis of biosignals. He is both a Chartered Engineer and Chartered Physicist.