An Optimal Solution to the Overfitting and Underfitting Problem of Healthcare Machine Learning Models

Abstract

In the current technological era, artificial intelligence is becoming increasingly popular. Machine learning, as the branch of AI is taking charge in every field such as healthcare, the Stock market, Automation, Robotics, Image Processing, and so on. In the current scenario, machine learning and/or deep learning are becoming very popular in medical science for disease prediction. Much research is underway in the form of disease prediction models by machine learning. To ensure the performance and accuracy of the machine learning model, it is important to keep some basic things in mind during training. The machine learning model has several issues which must be rectified duration of the training of the model so that the learning model works efficiently such as model selection, parameter tuning, dataset splitting, cross-validation, bias-variance tradeoff, overfitting, underfitting, and so on. Under- and over-fitting are the two main issues that affect machine learning models. This research paper mainly focuses on minimizing and/or preventing the problem of overfitting and underfitting machine learning models.