An artificial intelligence approach for breast cancer treatment

Abstract

Breast cancer is a pressing health concern that demands comprehensive research to understand its risk factors and improve patient outcomes. In this thesis, we undertake a multidimensional analysis to explore the relationship between women's properties and breast cancer occurrence. Leveraging a dataset collected from a reputable clinic, we employ advanced machine learning techniques to identify significant risk factors and develop predictive models. The ultimate goal is to enhance our understanding of breast cancer etiology and contribute to the development of targeted interventions. The thesis begins with a thorough review of existing literature on breast cancer risk factors, epidemiology, and the application of machine learning in the field. This review provides a theoretical foundation for our research and identifies gaps in knowledge, setting the stage for our investigation. We start by collecting a comprehensive dataset from the clinic, comprising anonymized patient records. The dataset encompasses a wide range of variables, including demographic information, lifestyle factors, genetic markers, and medical history. Proper data preprocessing and feature engineering techniques are applied to ensure the integrity and quality of the analysis. Next, we employ advanced machine learning algorithms such as logistic regression, decision trees and neural networks to develop predictive models. These models utilize the dataset to identify patterns and accurately predict the likelihood of breast cancer occurrence. To interpret the results and assess the significance of women's properties, we conduct in-depth analyses and comparisons with existing knowledge. The findings shed light on the influential risk factors associated with breast cancer occurrence, providing valuable insights for preventive strategies, early detection, and targeted interventions. In addition to the analysis of breast cancer risk factors, we incorporate a case study on value stream mapping in a radiology department. Value stream mapping, a lean management technique, is applied to identify bottlenecks, eliminate waste, and optimize processes in the radiology department. The case study highlights the practical application of value stream mapping in improving efficiency and patient flow, ultimately enhancing the overall quality of care in the radiology department. Through this comprehensive research, we aim to advance our understanding of breast cancer etiology, improve risk assessment models, and facilitate the development of personalized prevention and treatment approaches. Furthermore, by incorporating a case study on value stream mapping, we demonstrate the practical applicability of lean management techniques in healthcare settings. The insights gained from this thesis have implications for breast cancer research, clinical practice, and healthcare management. The identification of significant risk factors can inform targeted screening programs, early detection strategies, and personalized interventions. Additionally, the application of value stream mapping techniques can enhance operational efficiency, optimize resource allocation, and improve patient care in radiology departments and other healthcare settings. In conclusion, this thesis represents a comprehensive investigation into the relationship between women's properties and breast cancer occurrence. Through the utilization of machine learning techniques and the inclusion of a value stream mapping case study, we contribute to the growing body of knowledge in breast cancer research and healthcare management. It is our hope that this work will make a meaningful impact in the fight against breast cancer and drive advancements in patient care and outcomes.