Streaming Feature Grouping and Selection (Sfgs) For Big Data Classification

Real-time data has always been an essential element for organizations when the quickness of data delivery is critical to their businesses. Today, organizations understand the importance of real-time data analysis to maintain benefits from their generated data. Real-time data analysis is also known as real-time analytics, streaming analytics, real-time streaming analytics, and event processing. Stream processing is the key to getting results in real-time. It allows us to process the data stream in real-time as it arrives. The concept of streaming data means the data are generated dynamically, and the full stream is unknown or even infinite. This data becomes massive and diverse and forms what is known as a big data challenge. In machine learning, streaming feature selection has always been a preferred method in the preprocessing of streaming data. Recently, feature grouping, which can measure the hidden information between selected features, has begun gaining attention. This dissertation’s main contribution is in solving the issue of the extremely high dimensionality of streaming big data by delivering a streaming feature grouping and selection algorithm. Also, the literature review presents a comprehensive review of the current streaming feature selection approaches and highlights the state-of-the-art algorithms trending in this area. The proposed algorithm is designed with the idea of grouping together similar features to reduce redundancy and handle the stream of features in an online fashion. This algorithm has been implemented and evaluated using benchmark datasets against state-of-the-art streaming feature selection algorithms and feature grouping techniques. The results showed better performance regarding prediction accuracy than with state-of-the-art algorithms

Applying Machine Learning Algorithms for the Analysis of Biological Sequences and Medical Records

The modern sequencing technology revolutionizes the genomic research and triggers explosive growth of DNA, RNA, and protein sequences. How to infer the structure and function from biological sequences is a fundamentally important task in genomics and proteomics fields. With the development of statistical and machine learning methods, an integrated and user-friendly tool containing the state-of-the-art data mining methods are needed. Here, we propose SeqFea-Learn, a comprehensive Python pipeline that integrating multiple steps: feature extraction, dimensionality reduction, feature selection, predicting model constructions based on machine learning and deep learning approaches to analyze sequences. We used enhancers, RNA N6- methyladenosine sites and protein-protein interactions datasets to evaluate the validation of the tool. The results show that the tool can effectively perform biological sequence analysis and classification tasks. Applying machine learning algorithms for Electronic medical record (EMR) data analysis is also included in this dissertation. Chronic kidney disease (CKD) is prevalent across the world and well defined by an estimated glomerular filtration rate (eGFR). The progression of kidney disease can be predicted if future eGFR can be accurately estimated using predictive analytics. Thus, I present a prediction model of eGFR that was built using Random Forest regression. The dataset includes demographic, clinical and laboratory information from a regional primary health care clinic. The final model included eGFR, age, gender, body mass index (BMI), obesity, hypertension, and diabetes, which achieved a mean coefficient of determination of 0.95. The estimated eGFRs were used to classify patients into CKD stages with high macro-averaged and micro-averaged metrics

An information assistant system for the prevention of tunnel vision in crisis management

In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions

A clinician’s guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML)

Developing functional machine learning (ML)-based models to address unmet clinical needs requires unique considerations for optimal clinical utility. Recent debates about the rigours, transparency, explainability, and reproducibility of ML models, terms which are defined in this article, have raised concerns about their clinical utility and suitability for integration in current evidence-based practice paradigms. This featured article focuses on increasing the literacy of ML among clinicians by providing them with the knowledge and tools needed to understand and critically appraise clinical studies focused on ML. A checklist is provided for evaluating the rigour and reproducibility of the four ML building blocks: data curation, feature engineering, model development, and clinical deployment. Checklists like this are important for quality assurance and to ensure that ML studies are rigourously and confidently reviewed by clinicians and are guided by domain knowledge of the setting in which the findings will be applied. Bridging the gap between clinicians, healthcare scientists, and ML engineers can address many shortcomings and pitfalls of ML-based solutions and their potential deployment at the bedside

Radiomics-Based Inter-Lesion Relation Network to Describe [18F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer

Advanced image analysis, specifically radiomics, has been recognized as a potential source of biomarkers for cancers. However, there are challenges to its application in the clinic, such as proper description of diseases where multiple lesions coexist. In this study, we aimed to characterize the intra-tumor heterogeneity of metastatic prostate cancer using an innovative approach. This approach consisted of a transformation method to build a radiomic profile of lesions extracted from [18F]FMCH PET/CT images, a qualitative assessment of intra-tumor heterogeneity of patients, and a quantitative representation of the intra-tumor heterogeneity of patients in terms of the relationship between their lesions’ profiles. We found that metastatic prostate cancer patients had lesions with different radiomic profiles that exhibited intra-tumor radiomic heterogeneity and that the presence of many radiomic profiles within the same patient impacted the outcome

Practical approaches to mining of clinical datasets : from frameworks to novel feature selection

Research has investigated clinical data that have embedded within them numerous complexities and uncertainties in the form of missing values, class imbalances and high dimensionality. The research in this thesis was motivated by these challenges to minimise these problems whilst, at the same time, maximising classification performance of data and also selecting the significant subset of variables. As such, this led to the proposal of a data mining framework and feature selection method. The proposed framework has a simple algorithmic framework and makes use of a modified form of existing frameworks to address a variety of different data issues, called the Handling Clinical Data Framework (HCDF). The assessment of data mining techniques reveals that missing values imputation and resampling data for class balancing can improve the performance of classification. Next, the proposed feature selection method was introduced; it involves projecting onto principal component method (FS-PPC) and draws on ideas from both feature extraction and feature selection to select a significant subset of features from the data. This method selects features that have high correlation with the principal component by applying symmetrical uncertainty (SU). However, irrelevant and redundant features are removed by using mutual information (MI). However, this method provides confidence in the selected subset of features that will yield realistic results with less time and effort. FS-PPC is able to retain classification performance and meaningful features while consisting of non-redundant features. The proposed methods have been practically applied to analysis of real clinical data and their effectiveness has been assessed. The results show that the proposed methods are enable to minimise the clinical data problems whilst, at the same time, maximising classification performance of data