Breast Cancer Detection by Extracting and Selecting Features Using Machine Learning

The cancer of the breast is a significant cause of female death worldwide, but especially in developing countries. For better results and higher survival rates, early diagnosis and screening are crucial. Machine learning (ML) methods can aid in the initialdiscovery and diagnosis of breast cancer by choosing the most informative elements from medical data and eliminating irrelevant ones. The approach of feature extraction involves taking unstructured data and extracting a representative set of characteristics that may be used to classify or forecast data. The aim is to decrease the dimensionality of the feature space while upholding or even refining the accuracy of the ML model. An artificial intelligence model is developed on the given features to categorize mammography images into benign and malignant groups. Different supervised learning techniques, including support vector machines, random forests, and artificial neural networks, are employed and contrasted in order to select the best-performing model. This research offers a comprehensive framework for utilizing machine learning methods to detect breast cancer. The technique demonstrates how it might assist radiologists in the early detection of breast cancer by effectively extracting and selecting critical characteristics that could improve patient outcomes and potentially save lives

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Previsão Inteligente das alterações metabólicas no cancro retal com base em modelos de machine e deep learning

Machine learning, broadly speaking, applies statistical methods to training data to automatically adjust the parameters of a model, rather than a programmer needing to set them manually. Deep Learning is a sub-area of Machine Learning that studies how to solve complex and intuitive problems. The methodologies adopted, using computational means, such as the machines learned and those understood in the world in specific contexts from previous experiences and based on the hierarchy of concepts, use the most used concepts for the form and efficient solution of more varied complex problems. The main objective in this work is to study various classification algorithms in the area of machine learning, and validate until these points can use a solution for choosing more accurate methods in the selection of tests and in new statistics to improve the therapeutic response. The data involved in the training of classification algorithms refer to all patients with metabolic diseases shredding between the years 2003-2021 and the retrospective part. The best classification algorithms to develop are used in the decision support system in the most effective way in choosing the appropriate therapy for each of the future patients who predicted an approximate rate of 20 patients per year.Machine Learning, em termos gerais, aplica métodos estatísticos aos dados de treino para ajustar automaticamente os parâmetros de um modelo, em vez de um programador necessitar de defini-los manualmente. Deep Learning é uma subárea de Machine Learning que estuda como solucionar problemas complexos e intuitivos. As metodologias propostas permitem, com recurso a meios computacionais, que as máquinas aprendam e compreendam o mundo em determinados contextos a partir de experiências anteriores e com base na hierarquia de conceitos possam compreender conceitos mais complexos de forma a solucionarem eficientemente A mais variadíssima gama de problemas. O principal objetivo neste trabalho consiste no estudo de vários algoritmos de classificação na área de machine learning de forma a validar até que ponto estes podem representar uma solução para a escolha de métodos mais precisos na selecção dos doentes e em novas estratégias para melhorar a resposta terapêutica. Os dados envolvidos para treino dos algoritmos de classificação referem-se a todos os doentes tratados com doenças metabólicas entre os anos 2003-2021 na parte retrospectiva. Os melhores algoritmos de classificação a desenvolver serão usados num sistema de apoio à decisão que ajude de forma mais efetiva na escolha da terapia adequada para cada um dos futuros pacientes que se prevê surgirem a uma taxa aproximada de 20 pacientes por ano

An Evaluation of the Wisconsin Breast Cancer Dataset using Ensemble Classifiers and RFE Feature Selection Technique

Breast cancer represents one of the deadliest diseases that records a high number of death rate annually. It is the most common type of cancer and the main cause of death among women worldwide. Machine learning (ML) approach is an effective way to classify data, especially in medical field. It is widely used for classification and analysis to make decisions. In this paper, a performance comparison between two ensemble ML classifiers: Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) on the Wisconsin Breast Cancer Dataset (WBCD) is conducted. The main objective of this study is to assess the correctness of the classifiers with respect to their efficiency and effectiveness in classifying the dataset. This was done by utilizing all and reduced features of the dataset that were generated with Recursive Feature Elimination (RFE) feature selection technique. Four metrics were used in the study: Accuracy, Precision, Recall and F1-Score to evaluate the classifiers. All experiments were executed within Anaconda Environment with Jupyter Notebook and conducted using Python programming language. Experimental result shows that XGBoost with 5 reduced feature using RFE feature selection technique gives the highest accuracy (99.02%) with lowest error rate