## 3 Results
### Knee Osteoarthritis Detection
#### 3.3.2 Hybrid Models
The model, which combined GBM, CNN predictions of tibofemoral osteoarthritis (KL score), and clinical data, achieved an average precision of 0.862 and an area under the curve (AUC) of 0.958. The model constructed using only the GBM classifier and clinical data achieved an average precision of 0.472 and an AUC of 0.806. In 2022, [PERSON] and [PERSON] [30] developed two methods of detecting knee osteoarthritis, designated as Deep Hybrid Learning (DHLI and DHL-II), which employ deep learning and machine learning. A pre-trained deep learning model and Principal Component Analysis (PCA) algorithm were employed to extract the requisite information, which was then classified according to binary or multiple classifications (grades 3, 4, and 5) using the Support Vector Machine (SVM) classifier. A total of 9786 radiographic images of knees from the OAI dataset were utilized in this study, with 80% allocated for training and 20% for testing. The deep learning model was constructed from the ground up and utilized transfer learning. The model comprises five blocks, each comprising a 2D convolution layer with 32 filters and a ReLU layer. The initial three blocks are succeeded by an average pooling layer and a drop-out layer with a rate of 0.25. The fourth block, comprising 64 filters, is followed by a max-pooling layer and a dropout layer with a rate of 0.5, while the fifth block, comprising 128 filters, is followed by a max-pooling layer and a dropout layer with a rate of 0.75. The model head comprises a flatten layer and a fully connected layer (FC), which is followed by a dropout layer with a rate of 0.5 and a softmax layer. Deep learning models were trained with a batch size of 256 images using the following parameters: \(\beta\)1 = 0.9, \(\beta\)2 = 0.999, Adam optimizer, a learning rate of 0.001, and both categorical cross-entropy and binary cross-entropy for binary and multi-class classification, respectively. The SVM classifier was constructed with optimal values of gamma (0.0001) and a penalty parameter (1000). The DHL-I model was constructed de novo and trained for 50 epochs to predict the severity of knee osteoarthritis in five distinct classes. The DHL-II model was constructed using transfer learning and trained for 20 epochs to classify knee osteoarthritis into severity levels 2, 3, and 4. The DHL-I model achieved an accuracy of 74.57%, while the DHL-II model yielded an accuracy of 90.8%, 87%, and 88% for binary, 3-class, and 4-class classification, respectively. In a more recent study, [PERSON] et al. [42] proposed the use of transfer learning models based on sequential convolutional neural networks (CNNs), Residual Neural Network (ResNet-50), and Visual Geometry Group (VGG16) for the classification of osteoarthritis from knee radiographs. The models were trained using 3836 radiographs obtained from Kaggle. The VGG16 model demonstrated a training accuracy of 99% and a testing accuracy of 92%. [PERSON] et al. [32] proposed a binary and 3-class classification system for the objective determination of the severity of knee osteoarthritis (KOA) from X-ray images. The approach employed six pre-trained deep neural network (DNN) models: VGG19, VGG16, MobileNetV2, ResNet101, DenseNet121, and InceptionResNetV2. The models were trained and tested on a total of 9786 knee images from the Osteoarthritis Initiative (OAI) dataset. The ResNet101 model exhibited the highest accuracy, achieving 89%. The other principal methodologies employed in recent years are presented in Table 6 in descending order of their efficacy in identifying knee osteoarthritis from radiographic images.