The article discusses how machine learning models used in healthcare can have issues like overfitting and underfitting, which can affect their accuracy. The main focus is on finding ways to prevent or minimize these problems. Researchers suggest that by carefully selecting models, tuning parameters, splitting datasets, and using techniques like cross-validation, the issues of overfitting and underfitting can be addressed. By doing so, machine learning models can work more efficiently and accurately in predicting diseases.