Toward better prediction of recurrence for Cushing’s disease: a factorization-machine based neural approach

• Published in: International journal of machine learning and cybernetics 2021-03, Vol.12 (3), p.625-633
• Main Authors: Fan, Yanghua, Li, Dongfang, Liu, Yifan, Feng, Ming, Chen, Qingcai, Wang, Renzhi
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Body mass index; Classification; Complex Systems; Computational Intelligence; Control; Datasets; Decision making; Decision trees; Engineering; Factorization; Gender; Health services; Hormones; Machine learning; Mechatronics; Mortality; Neural networks; Original Article; Pattern Recognition; Recommender systems; Robotics; Sinuses; Surgery; Systems Biology; Tumors
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-020-01192-6

Cushing's disease (CD) is a rare disease that occurs in 1.2–1.4 persons per million population per year. Recurrence prediction after transsphenoidal surgery (TSS) is important for determining individual treatment and follow-up strategies. Between 2000 and 2017, 354 CD patients with initial postoperative remission and long-term follow-up data were enrolled from Peking union medical college hospital (PUMCH) to predict recurrence, and PUMCH is one of the largest CD treatment centers in the world. We first investigated the effect of a factorization machine (FM)-based neural network to predict recurrence after TSS for CD. This method could automatically reduce a portion of the cross-feature selection work with acceptable parameters. We conducted a performance comparison of various algorithms on the collected dataset. To address the lack of interpretability of neural network models, we also used the local interpretable model-agnostic explanations approach, which provides an explanation in the form of relevant features of the predicted results by approximating the model behavior of the variables in a local manner. Compared with existing methods, the DeepFM model obtained the highest AUC value (0.869) and the lowest log loss value (0.256). According to the importance of each feature, three top features for the DeepFM model were postoperative morning adrenocorticotropic hormone level, age, and postoperative morning serum cortisol nadir. In the post hoc explanation phase, the above-mentioned importance-leading features made a great contribution to the prediction probability. The results showed that deep learning-based models could better aid neurosurgeons in recurrence prediction after TTS for patients with CD, and could contribute to determining individual treatment strategies.