A Prediction Approach for Small Healthcare Dataset

Small dataset prediction is a challenging task in machine learning, particularly in cases where the number of samples is limited or the number of features is high. The problem arises because the model may overfit the data or not capture the true underlying patterns in the data. To address this challenge, various methods have been developed, including regularization techniques, data augmentation, and transfer learning. Regularization techniques such as Lasso, Ridge, and Elastic Net can help prevent overfitting by penalizing large weights. Data augmentation techniques such as image rotations, translations, and flips can be used to increase the effective size of the training set. Transfer learning techniques, on the other hand, enable models to learn from similar datasets and transfer that knowledge to the target dataset. Predicting outcomes or making informed decisions based on small datasets can be challenging. Small datasets can result in biased or incomplete models, making it difficult to make accurate predictions. Creating extended data to enlarge the small dataset will improve the ability of the prediction models and may increase its accuracy.