An Efficient Design of 2-D Digital Filters Using Singular Value Decomposition and Genetic Algorithm with Canonical Signed Digit (CSD) Coefficients

In this thesis, the design of 2-D filters by SVD is proposed. This technique reduces the complexity of the designed 2-D digital filters by decomposing it into a set of 1-D digital filters in zl and z2 connected in cascade. The design by SVD can be improved by varying the order of 1-D digital filters in each section based on their corresponding singular values. It is shown that by assigning higher order filters to the sections with greater singular values (SVs), and lower order filters to the sections with lower SVs, a sizable reduction in the total number of required multiplications is achieved. A Genetic Algorithm (GA) is used to design each of the 1-D filters instead of classical optimization. Canonical signed digit system is used to represent filters\u27 coefficients. CSD helps to improve the efficiency of multiplications and thus increase the throughput rate. Examples are provided to demonstrate the effectiveness and usefulness of the proposed technique

Machine Learning Approaches for Healthcare Analysis

Machine learning (ML)is a division of artificial intelligence that teaches computers how to discover difficult-to-distinguish patterns from huge or complex data sets and learn from previous cases by utilizing a range of statistical, probabilistic, data processing, and optimization methods. Nowadays, ML plays a vital role in many fields, such as finance, self-driving cars, image processing, medicine, and Speech recognition. In healthcare, ML has been used in applications such as the detection, prognosis, diagnosis, and treatment of diseases due to Its capability to handle large data. Moreover, ML has exceptional abilities to predict disease by uncovering patterns from medical datasets. Machine learning and deep learning are better suited for analyzing medical datasets than traditional methods because of the nature of these datasets. They are mostly large and complex heterogeneous data coming from different sources, requiring more efficient computational techniques to handle them. This dissertation presents several machine-learning techniques to tackle medical issues such as data imbalance, classification and upgrading tumor stages, and multi-omics integration. In the second chapter, we introduce a novel method to handle class-imbalanced dilemmas, a common issue in bioinformatics datasets. In class-imbalanced data, the number of samples in each class is unequal. Since most data sets contain usual versus unusual cases, e.g., cancer versus normal or miRNAs versus other noncoding RNA, the minority class with the least number of samples is the interesting class that contains the unusual cases. The learning models based on the standard classifiers, such as the support vector machine (SVM), random forest, and k-NN, are usually biased towards the majority class, which means that the classifier is most likely to predict the samples from the interesting class inaccurately. Thus, handling class-imbalanced datasets has gained researchers’ interest recently. A combination of proper feature selection, a cost-sensitive classifier, and ensembling based on the random forest method (BCECSC-RF) is proposed to handle the class-imbalanced data. Random class-balanced ensembles are built individually. Then, each ensemble is used as a training pool to classify the remaining out-bagged samples. Samples in each ensemble will be classified using a class-sensitive classifier incorporating random forest. The sample will be classified by selecting the most often class that has been voted for in all sample appearances in all the formed ensembles. A set of performance measurements, including a geometric measurement, suggests that the model can improve the classification of the minority class samples. In the third chapter, we introduce a novel study to predict the upgrading of the Gleason score on confirmatory magnetic resonance imaging-guided targeted biopsy (MRI-TB) of the prostate in candidates for active surveillance based on clinical features. MRI of the prostate is not accessible to many patients due to difficulty contacting patients, insurance denials, and African-American patients are disproportionately affected by barriers to MRI of the prostate during Active surveillance [6,7]. Modeling clinical variables with advanced methods, such as machine learning, could allow us to manage patients in resource-limited environments with limited technological access. Upgrading to significant prostate cancer on MRI-TB was defined as upgrading to G 3+4 (definition 1 - DF1) and 4+3 (DF2). For upgrading prediction, the AdaBoost model was highly predictive of upgrading DF1 (AUC 0.952), while for prediction of upgrading DF2, the Random Forest model had a lower but excellent prediction performance (AUC 0.947). In the fourth chapter, we introduce a multi-omics data integration method to analyze multi-omics data for biomedical applications, including disease prediction, disease subtypes, biomarker prediction, and others. Multi-omics data integration facilitates collecting richer understanding and perceptions than separate omics data. Our method is constructed using the combination of gene similarity network (GSN) based on Uniform Manifold Approximation and Projection (UMAP) and convolutional neural networks (CNNs). The method utilizes UMAP to embed gene expression, DNA methylation, and copy number alteration (CNA) to a lower dimension creating two-dimensional RGB images. Gene expression is used as a reference to construct the GSN and then integrate other omics data with the gene expression for better prediction. We used CNNs to predict the Gleason score levels of prostate cancer patients and the tumor stage in breast cancer patients. The results show that UMAP as an embedding technique can better integrate multi-omics maps into the prediction model than SO

Machine learning-based prediction of upgrading on magnetic resonance imaging targeted biopsy in patients eligible for active surveillance

OBJECTIVE: To examine the ability of machine learning methods to predict upgrading of Gleason score on confirmatory magnetic resonance imaging-guided targeted biopsy (MRI-TB) of the prostate in candidates for active surveillance. SUBJECTS AND METHODS: Our database included 592 patients who received prostate multiparametric magnetic resonance imaging in the evaluation for active surveillance. Upgrading to significant prostate cancer on MRI-TB was defined as upgrading to G 3+4 (definition 1 - DF1) and 4+3 (DF2). Machine learning classifiers were applied on both classification problems DF1 and DF2. RESULTS: Univariate analysis showed that older age and the number of positive cores on pre-MRI-TB were positively correlated with upgrading by DF1 (P-value ≤ 0.05). Upgrading by DF2 was positively correlated with age and the number of positive cores and negatively correlated with body mass index. For upgrading prediction, the AdaBoost model was highly predictive of upgrading by DF1 (AUC 0.952), while for prediction of upgrading by DF2, the Random Forest model had a lower but excellent prediction performance (AUC 0.947). CONCLUSION: We show that machine learning has the potential to be integrated in future diagnostic assessments for patients eligible for AS. Training our models on larger multi-institutional databases is needed to confirm our results and improve the accuracy of these models\u27 prediction