The escalating incidence of thyroid cancer over the past decade underscores the imperative for effective classification and early detection of thyroid nodules. An automated system for this purpose could significantly aid physicians, particularly in expediting diagnostic processes. However, attaining this objective has proven challenging, primarily attributed to the constrained dataset size for medical images and the laborious process of feature extraction. This research addresses these challenges by thoroughly exploring the importance of extracting meaningful features for tumor detection and introducing a quantum-based convolutional neural network. The proposed approach employs a quantum filter transformation for intricate feature extraction, coupled with a classical neural network for the classification of thyroid nodules in ultrasound images. The classification process involves two distinct categorizations: distinguishing between nodules that are benign or malignant, and identifying the specific suspicious class to which the nodule is attributed. The amalgamation of both classifiers yields a comprehensive characterization of thyroid nodules, showcasing notable accuracy. For tumor classification, the model achieves an accuracy of 97.63%, precision of 97.72%, and recall of 97.64% on a test dataset containing 127 images. Similarly, for suspicious level classification, the model attains an accuracy of 93.87%, precision of 94.58%, and recall of 93.88% on a test dataset containing 98 images. These results surpass the performance of existing models, marking a significant advancement in the field of thyroid nodule classification. The proposed model represents a promising and innovative methodology that could offer valuable support mechanisms in clinical settings, facilitating the rapid classification and diagnosis of thyroid cancer.

In recent years, machine learning is attaining higher precision and accuracy in clinical heart disease dataset classification. However, literature shows that the quality of heart disease feature used for the training model has a significant impact on the outcome of the predictive model. Thus, this study focuses on exploring the impact of the quality of heart disease features on the performance of the machine learning model on heart disease prediction by employing recursive feature elimination with cross-validation (RFECV). Furthermore, the study explores heart disease features with a significant effect on model output. The dataset for experimentation is obtained from the University of California Irvine (UCI) machine learning dataset. The experiment is implemented using a support vector machine (SVM), logistic regression (LR), decision tree (DT), and random forest (RF) are employed. The performance of the SVM, LR, DT, and RF models. The result appears to prove that the quality of the feature significantly affects the performance of the model. Overall, the experiment proves that RF outperforms as compared to other algorithms. In conclusion, the predictive accuracy of 99.7% is achieved with RF.