## 3 Results
### Knee Osteoarthritis Detection
#### 3.3.2 Hybrid Models  
The combination of deep learning and traditional machine learning models for the identification of knee osteoarthritis and the integration of other clinical data have yielded variable results. Nonetheless, the integration of feature extractors has emerged as a valuable experimental approach. [PERSON] et al. [12] employed an automated computer-aided diagnostic approach for the detection of knee osteoarthritis, utilizing predictive modeling based on multivariate linear regression (MLR) and the independent component analysis approach (ICA) to extract features and circumvent the issue of limited datasets. The resulting characteristics were employed for the fully automated classification task, which was conducted using Naive Bayes and Random Forest classifiers. The proposed system achieved an accuracy of 82.98%, a sensitivity of 87.15%, and a specificity of 80.65%. [PERSON] et al. [40] employed Elastic Net (EN) and Random Forest (RF) to develop a predictive model for knee osteoarthritis. This model was constructed using patient assessment data (i.e., signs and symptoms of the knees and medication use) and a convolutional neural network (CNN). The model comprises four convolutional layers. Each convolutional layer is followed by a batch normalization layer (BN), a rectified linear unit (ReLU) activation layer, and a max-pooling layer. The final max-pooling layer is followed by a fully connected layer (FC) comprising 1024 neurons and a softmax layer with five outputs, representing the five KL grades of KOA gravity. To prevent over-fitting, a dropout layer with a rate of 0.25 is included after the final convolutional layer, and a dropout layer with a rate of 0.5 is applied after the fully connected layer. The use of 100 trees in this work has yielded the lowest root-mean-square error (RMSE) in the validation set. The Elastic Net and Random Forest regression models yielded a global RMSE of 0.974 and 0.943, respectively. [PERSON] et al. [6] have compared the performance of K-Nearest Neighbors (K-NN) and Decision Tree in knee osteoarthritis prediction when creating their dataset known as Medical-expert, which consisted of 1650 knee radiographic images collected from various hospitals and diagnostic centers in India. In this study, the region of interest (ROI) was segmented using Sobel and Prewitt edge detectors, which yielded the most optimal results. The Active Shape Model (ASM) with a 3 \(\times\) 3 mask and the Otsu method were employed to extract the cartilage area. Subsequently, an algorithm was employed to assess cartilage thickness based on pixel density. Both models were trained on 50% of the dataset and tested on the remaining 50%. The decision tree model achieved a global accuracy of 95.09%, whereas the KNN model yielded a global accuracy of 99.81%. In 2021, [PERSON] et al. [41] developed multiple models, trained either individually or in groups, to predict femoropatellar osteoarthritis (PFOA and non-PFOA). All models were trained on 18,436 knee X-ray images in lateral views acquired from the MOST dataset. The models were developed using an end-to-end deep convolution neural network and a trained Gradient Boosting Machine (GBM) classifier, which were both informed by clinical data, including age, sex, body mass index (BMI), the Western Ontario and McMaster Universities Arthritis Index (WOMAC) total score, and the KL grade of tibofemoral osteoarthritis. In this study, faster R-CNN was employed as a means of identifying the region of interest pertaining to the patella. The Deep-CNN model comprises three blocks of convolution layers, trained over more than 20 epochs using a stochastic gradient descent algorithm. Each convolution layer is followed by a batch normalization (BN) layer, a max-pooling layer, and a rectified linear unit (ReLU) activation function. The third block is followed by two fully connected layers, separated by a dropout layer with a rate of 0.5.