The dataset used for training and testing the model is a CSV file taken from "https://www.kaggle.com/datasets/merishnasuwal/breast-cancer-prediction-dataset" , which contains various attributes related to breast cancer diagnosis. The dataset is pre-processed to obtain relevant information for the model to make predictions.
Breast cancer detection using artificial neural networks (ANNs) is a challenging task due to several reasons. In this answer, I will discuss some of the challenges I ran into while working with breast cancer detection using ANNs.
Data Imbalance:
Data imbalance is a common problem in breast cancer detection, where the number of positive (cancer) samples is much lower than the number of negative (non-cancer) samples. This imbalance can lead to biased models that tend to classify most samples as negative. To overcome this, data augmentation techniques like oversampling or undersampling can be used to balance the dataset.
Feature Selection:
Breast cancer detection requires a large number of features, such as the size and shape of the tumor, the presence of microcalcifications, and the texture of the breast tissue. Selecting the most relevant features is essential to improve the performance of the model. This requires domain knowledge and expertise to identify the most relevant features.
Overfitting:
Overfitting is a common problem in machine learning, where the model performs well on the training data but poorly on the testing data. Overfitting can occur in ANNs when the model is too complex, leading to high variance. Regularization techniques like L1 and L2 regularization, dropout, or early stopping can be used to prevent overfitting.
Technologies used
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