The main aim of this project is to design and implement an intelligent breast cancer detection system that uses Support Vector Machine (SVM) algorithms to predict the likelihood of cancer based on medical data inputs. With SVM achieving an accuracy rate as high as 97.2% in preliminary studies, the goal is to train the model on breast cancer datasets, improve its predictive accuracy, and potentially expand its application to other diseases. This tool is intended to act as a decision-support system for healthcare practitioners, ensuring faster and more accurate diagnoses. However, the model's outputs should always be validated by medical professionals. Students undertaking this project will gain insights into the application of AI in healthcare, specifically in diagnostic modeling, and understand the societal impact of machine learning in medical practice.
This twelve-week project begins with understanding the foundational concepts of machine learning and SVMs. Students will start by installing required tools such as Python and Anaconda, and set up a working environment using platforms like Google Colab. They will then identify and prepare medical datasets (e.g., from Kaggle), preprocess the data, and build a basic model framework using SVM.
The following weeks will be focused on training the model, testing it against unseen data, and refining the algorithm to boost its predictive accuracy. Students will explore various feature selection techniques, library tools, and visualization methods to enhance model interpretability. The final phase of the project includes system testing, documentation of the workflow and results, and a team presentation. Ethical considerations must be maintained, especially regarding the sensitive nature of medical data and the risk of misdiagnosis if the model is inaccurately trained.
