AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia

Guillaume Chassagnon; Maria Vakalopoulou; Enzo Battistella; Stergios Christodoulidis; Trieu-Nghi Hoang-Thi; Severine Dangeard; Eric Deutsch; Fabrice Andre; Enora Guillo; Nara Halm; Stefany El Hajj; Florian Bompard; Sophie Neveu; Chahinez Hani; Ines Saab; Alienor Campredon; Hasmik Koulakian; Souhail Bennani; Gael Freche; Aurelien Lombard; Laure Fournier; Hippolyte Monnier; Teodor Grand; Jules Gregory; Antoine Khalil; Elyas Mahdjoub; Pierre-Yves Brillet; Stephane Tran Ba; Valerie Bousson; Marie-Pierre Revel; Nikos Paragios

Subsequently, this reduced feature space was considered to be most appropriate for training, and the following 7 classification methods with acceptable performance, > 60% in terms of balanced accuracy, as well as coherent performance between training and validation, performance decrease < 20% for the balanced accuracy between training and validation, were trained and combined together through a winner takes all approach to determine the optimal outcome ( Table 4 ). The final selected methods include the {Linear, Polynomial Kernel, Radial Basis Function} Support Vector Machines, Decision Trees, Random Forests, AdaBoost, and Gaussian Naive Bayes which were trained and combined together through a winner takes all approach to determine the optimal outcome. To overcome the unbalance of the different classes, each class received a weight inversely proportional to its size. The Support Vector Machines were all three granted a polynomial kernel function of degree 3 and a penalty parameter of 0.25. In addition, the one with a Radial Basis Function kernel was granted a kernel coefficient of 3. The decision tree classifier was limited to a depth of 3 to avoid overfitting. The random forest classifier was composed of 8 of such trees. AdaBoost classifier was based on a decision tree of maximal depth of 2 boosted three times.