Prior research has provided evidence that the disclosure of internal controls material weaknesses (ICMWs) is a powerful input attribute in misstatement prediction. However, the disclosure of ICMWs is imperfect in capturing internal control quality because many firms with control problems fail to disclose ICMWs on a timely basis. The purpose of this study is to examine whether the existence of ICMWs, including both the disclosed and the undisclosed ICMWs, improves misstatement prediction. I develop a two-stage machine learning model for misstatement prediction with the predicted existence of ICMWs as the intermediate concept; my model that outperforms the model with the ICMW disclosures. I also find that the model incorporating both the predicted existence and the disclosure of ICMWs outperforms those with only the disclosure or the predicted existence of ICMWs. These results hold across different input attributes, machine learning methods, and prediction periods, and training-test samples splitting methods. Finally, this study shows that the two-stage models outperform the one-stage models in predictions related to financial reporting quality.