Enhancing Breast Cancer Detection with Neural Networks: A Machine Learning Approach for Improved Diagnostic Accuracy
Vikrant Aadiwal, Bhisham Sharma, Dhirendra Prasad Yadav · 2024
This research paper presents a neural network model designed for breast cancer detection, which has demonstrated exceptional accuracy in distinguishing between malignant and benign cases. The model achieved an impressive 98.25% accuracy, with F1 Score, Precision, and Recall all reaching 0.9825, showcasing its high reliability. The confusion matrix underscores the model’s effectiveness in minimizing both false positives and false negatives, while the close alignment between training and validation metrics suggests robust generalization to unseen data, mitigating concerns about overfitting.These findings underline the significant impact that advanced machine learning techniques can have in the realm of medical diagnostics, particularly in the early detection and treatment planning of breast cancer. Given its high accuracy and reliability, this model has the potential to become a valuable tool in clinical settings, assisting healthcare professionals in making well-informed decisions and ultimately improving patient outcomes. By advancing Good Health and Well-being and contributing to Reduced Inequalities, this research emphasizes the role of innovative technologies in enhancing healthcare delivery and ensuring equitable access to high-quality medical care.