Independent Feature Sets Research Articles

Enhanced Diabetes Prediction and Monitoring through Integration of Multimodal Data Using XGBoost

Abstract: Diabetes is a critical and become more complicated disease that can cause serious health problems if it is not adequately managed. The early diagnosis and treatment of diabetes is a critical component of the condition that can be greatly aided by data analysis and predictive algorithms. Through the use of data mining techniques, such as classification and prediction models, it is possible to analyse various elements of diabetes data and extract useful information that can be used for the early detection and prediction of the condition. One machine learning technique that can effectively and highly precisely predict diabetes is the XGBoost classifier. This method makes use of the gradient-boosting architecture and can handle large and intricate datasets with independent high-dimensional feature sets. Conversely, it is crucial to remember that the choice of the best algorithm for diabetes prediction could depend on the specifics of the data as well as the area of study being investigated. Data analysis and prediction methods can be applied not only to anticipate diabetes but also to monitor the disease's progression, find risk factors for diabetes and its complications, and assess the effectiveness of treatment. By using these techniques, medical professionals can obtain important insights into the disease's underlying causes, which helps them make informed decisions about patient management. The early detection and management of diabetes, a chronic disease that is rapidly expanding and poses major health risks, has the potential to be significantly improved through the application of data analysis and prediction algorithms. An accuracy rate of 89% was achieved by the XGBoost classifier, which demonstrated the highest level of performance.

Construction of statistical recognition algorithms based on two-dimensional threshold functions

The recognition problem often rises in the classification of multidimensional data and is an integral part of artificial intelligence. Currently, there are many well-known methods and algorithms for solving the recognition problem. However, these methods are mainly aimed at solving such problems where objects are described by a set of independent features. A large number of features describe recognition problems in various fields of human activity. In such problems, the probability of violating the assumption of feature independence is quite high and this determines the relevance of this study.The purpose of this study is to develop a model of recognition algorithms based on the method of making statistical decisions, taking into account the high dimensionality of feature space.A statistical approach to solving the problem of recognition under the conditions of the high dimensionality of feature space is proposed. On the basis of this approach, a model of statistical recognition algorithms based on the construction of two-dimensional threshold rule is proposed. The main advantage of the proposed model is the reduction in the volume of computational operations when recognizing objects in the test set. At that, the reduction of the volume of computational operations is ensured using a number of procedures: selection of representative features; building a two-dimensional threshold rule; determination of a set of reference two-dimensional threshold rules; determination of basic two-dimensional threshold rules and calculation of a probabilistic assessment of the object belonging. This characteristic is very important for real-time recognition systems.The operability of the proposed model of recognition algorithms was investigated in the course of experiments in solving a model problem and the problem of recognizing a person's identity from a signature image. The results of the computational experiment confirm the operability of the proposed approach in solving the recognition problem under conditions when the assumption of feature independence is not satisfied. The results of experimental studies show that the proposed statistical algorithms increase the accuracy of recognition and significantly reduce the volume of computational operations by recognizing an unknown object specified in the space of interrelated features. These algorithms can be used in the compilation of various software systems focused on solving applied recognition problems in the conditions of the high dimensionality of feature space.

Machine Learning Approach for Diagnosis and Prognosis of Cardiac Arrhythmia Condition Using a Minimum Feature Set and Auto-Segmentation-Based Window Optimisation

Cardiovascular diseases have become extremely prevalent in the global population. Several accurate classification methods for arrhythmias have been proposed in the healthcare literature. However, extensive research is required to improve the prediction accuracy of various arrhythmia conditions. In this paper, discussion is focussed on two major objectives: optimisation of windows based on our proposed auto-segmentation method for the exact diagnosis of the heart condition within the segment and prediction of arrhythmia progression. For prediction, identification of features is vital. Identified efficient independent feature sets such as RR interval, peak-to-peak amplitude, and unique derived parameters such as coefficient of variation (CV) of RR interval and CV of peak-to-peak amplitude. The progression of arrhythmia includes the following steps such as data preprocessing, time and frequency domain feature extraction, and feature selection using principal component analysis. A hypertuned support vector machine is utilised for accurate diagnosis. Proposed two techniques to predict the progression of arrhythmias: the regression-based trend curve (RBTC) and the fuzzy enhanced Markov model (FEMM). We have effectively evaluated our prediction algorithms using offline Massachusetts Institute of Technology Physio Net database signals, using automatic segmentation with prediction accuracy of 98 %. In terms of accuracy, FEMM outperforms RBTC. Thus, an auto-segmentation algorithm was proposed to classify various arrhythmia signals using a minimal feature set and to predict future conditions using our proposed method, FEMM.

Prospectivity modelling of critical mineral deposits using a generative adversarial network with oversampling and positive-unlabelled bagging

The demand for critical minerals is rapidly increasing worldwide, yet future global supply remains uncertain due to the difficulty in discovering new deposits using traditional methods. To increase the success rate of exploration projects for these vital resources, the use of artificial intelligence is continuously increasing for big and complex data analysis. This study proposes a new machine learning-based framework that tackles common problems associated with exploring critical mineral deposits, such as the shortage of known mineral occurrences, challenges in selecting negative samples in barren regions, and unbalanced training data. Our framework combines an improved generative adversarial network with positive and unlabelled learning to enhance efficiency. To test the performance of the framework, we create prospectivity maps of mafic–ultramafic intrusion-hosted mineralisation for cobalt, chromium, and nickel in the Gawler Craton, South Australia. The models are trained on a carefully selected set of independent features based on a conceptual model derived from open-access exploration data, resulting in high and stable performance. The prospectivity maps show a strong spatial correlation between high probabilities and known mineral occurrences and predict potential greenfield regions for future exploration. Our models demonstrate a significantly higher accuracy compared to a conventional approach using a standard random forest classifier and reveal that geophysical features play a crucial role in mapping prospective regions of critical minerals. Overall, our framework has the potential to significantly enhance critical mineral exploration by providing a more accurate and efficient approach to identifying prospective regions for future mining operations.

HIDR: A Hierarchical Independent Detection and Recognition strategy for underwater acoustic multi-target recognition

This paper proposed HIDR (Hierarchical Independent Detection and Recognition), a novel strategy for underwater acoustic multi-target recognition. HIDR adopts a hierarchical architecture instead of the single-step traditional target recognition task, which is a relatively challenging task especially for the multi-target recognition. HIDR would firstly distinguish target signals from non-target signals as a detection stage, then pinpoint the specific target from all possible types of target signals as a recognition stage. This hierarchical architecture cannot only sharply reduce the difficulty of feature extraction, but also benefit for the higher recognition accuracy. A universal feature extraction strategy is utilized to extract two independent feature sets for the two stages respectively, which include auditory parameters especially adaptive to detection/recognition stages, as well as other common time/frequency domain features. Experiments with real sea trial data are conducted to compare HIDR with existing high-quality multi-target recognition schemes. Experimental results show that the proposed strategy can reach up to 88.71% of recognition accuracy in four-class recognition task, which is more than 5.25% higher than that of mainstream methods.

Deep Feature Meta-Learners Ensemble Models for COVID-19 CT Scan Classification

The infectious nature of the COVID-19 virus demands rapid detection to quarantine the infected to isolate the spread or provide the necessary treatment if required. Analysis of COVID-19-infected chest Computed Tomography Scans (CT scans) have been shown to be successful in detecting the disease, making them essential in radiology assessment and screening of infected patients. Single-model Deep CNN models have been used to extract complex information pertaining to the CT scan images, allowing for in-depth analysis and thereby aiding in the diagnosis of the infection by automatically classifying the chest CT scan images as infected or non-infected. The feature maps obtained from the final convolution layer of the Deep CNN models contain complex and positional encoding of the images’ features. The ensemble modeling of these Deep CNN models has been proved to improve the classification performance, when compared to a single model, by lowering the generalization error, as the ensemble can meta-learn from a broader set of independent features. This paper presents Deep Ensemble Learning models to synergize Deep CNN models by combining these feature maps to create deep feature vectors or deep feature maps that are then trained on meta shallow and deep learners to improve the classification. This paper also proposes a novel Attentive Ensemble Model that utilizes an attention mechanism to focus on significant feature embeddings while learning the Ensemble feature vector. The proposed Attentive Ensemble model provided better generalization, outperforming Deep CNN models and conventional Ensemble learning techniques, as well as Shallow and Deep meta-learning Ensemble CNNs models. Radiologists can use the presented automatic Ensemble classification models to assist identify infected chest CT scans and save lives.

Comparison of classifiers using robust features for depression detection on Bahasa Malaysia speech

Early detection of depression allows rapid intervention and reduce the escalation of the disorder. Conventional method requires patient to seek diagnosis and treatment by visiting a trained clinician. Bio-sensors technology such as automatic depression detection using speech can be used to assist early diagnosis for detecting remotely those who are at risk. In this research, we focus on detecting depression using Bahasa Malaysia language using speech signals that are recorded remotely via subject’s personal mobile devices. Speech recordings from a total of 43 depressed subjects and 47 healthy subjects were gathered via online platform with diagnosis validation according to the Malay beck depression inventory II (Malay BDI-II), patient health questionnaire (PHQ-9) and subject’s declaration of major depressive disorder (MDD) diagnosis by a trained clinician. Classifier models were compared using time-based and spectrum-based microphone independent feature set with hyperparameter tuning. Random forest performed best for male reading speech with 73% accuracy while support vector machine performed best on both male spontaneous speech and female reading speech with 74% and 73% accuracy, respectively. Automatic depression detection on Bahasa Malaysia language has shown to be promising using machine learning and microphone independent features but larger database is necessary for further validation and improving performance.

Real-Time Measurement of Seam Strength Using Optical Spectroscopy for Al–Li Alloy in Laser Beam Welding

This article investigates a real-time nondestructive measurement method of the seam strength using the optical spectroscopy and ensemble learning in laser beam welding. First, a linear positive relationship between the seam strength and acquired optical spectrum signal was established. Then, a new feature reduction method was proposed to extract the independent feature set with low correlation and high diversity. Finally, a hybrid classification model is proposed based on fast independent component analysis (ICA) and extremely randomized trees (ET). The model was thoroughly verified with various welding experiments and careful comparison with decision tree (DT), extreme gradient boosting DT (XGBoost), and support vector machine (SVM). More importantly, driven by the monitoring data and random forest with fast independent component analysis (RFICA)-ET model, it was found that Al I at 669.8673 nm and Ar I at 610.5635 nm were the two key elements in the dynamic plasma for accurate seam strength measurement.

Diagnostic prediction of autism spectrum disorder using complex network measures in a machine learning framework

Objective imaging-based biomarker discovery for psychiatric conditions is critical for accurate diagnosis and treatment. Using a machine learning framework, this work investigated the utility of brain’s functional network topology (complex network features) extracted from functional magnetic resonance imaging (fMRI) functional connectivity (FC) as viable biomarker of autism spectrum disorder (ASD). To this end, we utilized resting-state fMRI data from the publicly available ABIDE dataset consisting of 432 ASD patients and 556 matched healthy controls. Upon standard pre-processing, 3D + time fMRI data were parcellated into 200 functionally homogenous regions, and whole-brain FC network using Pearson’s correlation was obtained from corresponding regional mean time series. A battery of complex network features were computed from the FC network using graph theoretic techniques. Recursive-Cluster-Elimination Support Vector Machine algorithm was employed to compare the predictive performance of three independent feature sets, (i) FC, (ii) complex network measures, and (iii) both combined. The study found that FC could diagnose ASD with 67.3 % accuracy and graph measures with 64.5 % accuracy, while the combined feature set could diagnose with 70.1 % accuracy (all accuracies were significantly different, p < 10−30). The most discriminative imaging features were mainly from lateral temporal, occipital, precuneus (all reduced in ASD) and orbito-frontal (elevated in ASD) regions. We concluded that network topology (graph measures) carried some unique information about ASD pathology not available in bivariate connectivity (FC), and that using both together provided better prediction than using individual measures. Future prediction studies could incorporate multiple fMRI analysis strategies within their framework to achieve superior prediction performances.

Different features of real-world objects are represented in a dependent manner in long-term memory.

In the present study, we examined how real-world objects are represented in long-term memory. Two contrasting views exist with regard to this question: one argues that real-world objects are represented as a set of independent features, and the other argues that they form bound integrate representations. In 5 experiments, we tested the different predictions of each view, namely whether the different features of real-world items are remembered and forgotten independently from each other, in a feature-based manner, or conversely are stored and lost in an object-based manner, with all features depending upon each other. Across various stimuli, learning tasks (incidental or explicit), experimental setups (within- or between-subjects design), feature-dimensions, and encoding times, we consistently found that information is forgotten in an object-based manner. When an object ceases to be fully remembered, all of its features are lost, instead of only some of the object's features being lost whereas other features are still remembered. Furthermore, we found support for a strong form of dependency among the different features, namely a hierarchical structure. We conclude that visual long-term memory is object-based, challenging previous findings. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A New Feature Selection Method for Text Classification Based on Independent Feature Space Search

Feature selection method is designed to select the representative feature subsets from the original feature set by different evaluation of feature relevance, which focuses on reducing the dimension of the features while maintaining the predictive accuracy of a classifier. In this study, we propose a feature selection method for text classification based on independent feature space search. Firstly, a relative document-term frequency difference (RDTFD) method is proposed to divide the features in all text documents into two independent feature sets according to the features’ ability to discriminate the positive and negative samples, which has two important functions: one is to improve the high class correlation of the features and reduce the correlation between the features and the other is to reduce the search range of feature space and maintain appropriate feature redundancy. Secondly, the feature search strategy is used to search the optimal feature subset in independent feature space, which can improve the performance of text classification. Finally, we evaluate several experiments conduced on six benchmark corpora, the experimental results show the RDTFD method based on independent feature space search is more robust than the other feature selection methods.

Use of NLP Based Combined Features for Sentiment Classification

Sentiment analysis is the technique of automatic detection of the belief or the mood of an author towards a certain subject in textual form. To extract the opinion present in text, the machine needs expertise in the area of natural language processing. In this paper, machine learning based document-level sentiment classification is performed on Amazon product reviews to classify them as positive and negative. Two NLP based feature extraction techniques (Word Relation and POS based) are used in this study to determine the features that are sentiment bearing. The features are extracted as basic features (unigrams, bigrams and trigrams) and their combinations (unigrams+bigrams, unigrams+trigrams, unigrams+bigrams+trigrams). In order to identify the features that are most informative and to bring down the computational time of the classification algorithms, feature selection techniques are used. Performance of independent and combined feature sets is assessed using accuracy, precision, recall and F-measure. From the experiments conducted, it is observed that combined features outperformed independent features using Boolean Multinomial Naive Bayes (BMNB) classifier.

Part of Speech Tagging in Urdu: Comparison of Machine and Deep Learning Approaches

In Urdu, part of speech (POS) tagging is a challenging task as it is both inflectionally and derivationally rich morphological language. Verbs are generally conceived a highly inflected object in Urdu comparatively to nouns. POS tagging is used as a preliminary linguistic text analysis in diverse natural language processing domains such as speech processing, information extraction, machine translation, and others. It is a task that first identifies appropriate syntactic categories for each word in running text and second assigns the predicted syntactic tag to all concerned words. The current work is the extension of our previous work. Previously, we presented conditional random field (CRF)-based POS tagger with both language dependent and independent feature set. However, in the current study, we offer: 1) the implementation of both machine and deep learning models for Urdu POS tagging task with well-balanced language-independent feature set and 2) to highlight diverse challenges which cause Urdu POS task a challenging one. In this research, we demonstrated the effectiveness of machine learning and deep learning models for Urdu POS task. Empirically, we have evaluated the performance of all models on two benchmark datasets. The core models evaluated in this study are CRF, support vector machine (SVM), two variants of the deep recurrent neural network (DRNN), and a variant of n-gram Markov model the bigram hidden Markov model (HMM). The two variants of DRRN models evaluated include forward long short-term memory (LSTM)-RNN and LSTM-RNN with CRF output.

Radiomics-guided therapy for bladder cancer: Using an optimal biomarker approach to determine extent of bladder cancer invasion from t2-weighted magnetic resonance images

BackgroundCurrent clinical staging methods are unable to accurately define the extent of invasion of localized bladder cancer, which affects the proper use of systemic therapy, surgery, and radiation. Our purpose was to test a novel radiomics approach to identify optimal imaging biomarkers from T2-weighted magnetic resonance imaging (MRI) scans that accurately classify localized bladder cancer into 2 tumor stage groups (≤T2 vs >T2) at both the patient level and within bladder subsectors. Method and MaterialsPreoperative T2-weighted MRI scans of 65 consecutive patients followed by radical cystectomy were identified. A 3-layer, shell-like volume of interest (VOI) was defined on each MRI slice: Inner (lumen), middle (bladder wall), and outer (perivesical tissue). An optimal biomarker method was used to identify features from 15,834 intensity and texture properties that maximized the classification of patients into ≤T2 versus >T2 groups. A leave-one-out strategy was used to cross-validate the performance of the identified biomarker feature set at the patient level. The performance of the feature set was then evaluated at the subsector level of the bladder by dividing the VOIs into 8 radial sectors. ResultsA total of 9 optimal biomarker features were derived and demonstrated a sensitivity, specificity, accuracy of prediction, and area under a receiver operating characteristic curve of 0.742, 0.824, 0.785, and 0.806, respectively, at the patient level and 0.681, 0.788, 0.763, and 0.813, respectively, at the radial sector level. All 9 selected features were extracted from the middle shell of the VOI and based on texture properties. ConclusionsAn approach to select a small, highly independent feature set that is derived from T2-weighted MRI scans that separate patients with bladder cancer into ≤T2 versus >T2 groups at both the patient level and within subsectors of the bladder has been developed and tested. With external validation, this radiomics approach could improve the clinical staging of bladder cancer and optimize therapeutic management.

Using textual features for the detection of vandalism in wikipedia: a comparative approach in low-resource language sections

This study investigates the impact of using textual features for the detection of vandalism across low-resource language sections in Wikipedia. For this purpose, we propose new features that allow the machine learning-based text classifiers to better distinguish vandalism and to improve the detection rates of vandalism across languages, based on textual features applied in previous researches. These features enable us to compare the contributions of the bots against vandalism, stressing the differences between bots and editors with regards to the detection of vandalism. We propose a new set of efficient and language independent features, which has the performance level similar to the previous sets. Three Wikipedia sections will be used for this purpose: Simple English (simple), Albanian (sq) and Bosnian (bs). We will show that our set of textual features has similar and, in some cases, better vandalism detection rates across languages than previous research.

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage.

Simplifying multidimensional fermentation dataset analysis and visualization: One step closer to capturing high-quality mutant strains

In this study, we analyzed mutants of Clostridium acetobutylicum, an organism used in a broad range of industrial processes related to biofuel production, to facilitate future studies of bioreactor and bioprocess design and scale-up, which are very important research projects for industrial microbiology applications. To accomplish this, we generated 329 mutant strains and applied principal component analysis (PCA) to fermentation data gathered from these strains to identify a core set of independent features for comparison. By doing so, we were able to explain the differences in the mutant strains’ fermentation expression states and simplify the analysis and visualization of the multidimensional datasets related to the strains. Our study has produced a high-efficiency PCA application based on a data analytics tool that is designed to visualize screening results and to support several hundred sets of data on fermentation interactions to assist researchers in more precisely screening and capturing high-quality mutant strains. More importantly, although this study focused on the use of PCA in microbial fermentation engineering, its results are broadly applicable.

Exploiting ensemble learning for automatic cataract detection and grading

Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach.

Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis

The goal of this study was to devise a machine learning methodology as a viable low-cost alternative to a second reader to help augment physicians' interpretations of breast ultrasound images in differentiating benign and malignant masses. Two independent feature sets consisting of visual features based on a radiologist's interpretation of images and computer-extracted features when used as first and second readers and combined by adaptive boosting (AdaBoost) and a pruning classifier resulted in a very high level of diagnostic performance (area under the receiver operating characteristic curve = 0.98) at a cost of pruning a fraction (20%) of the cases for further evaluation by independent methods. AdaBoost also improved the diagnostic performance of the individual human observers and increased the agreement between their analyses. Pairing AdaBoost with selective pruning is a principled methodology for achieving high diagnostic performance without the added cost of an additional reader for differentiating solid breast masses by ultrasound.

A Novel Feature Selection Technique for Text Classification Using Naïve Bayes.

With the proliferation of unstructured data, text classification or text categorization has found many applications in topic classification, sentiment analysis, authorship identification, spam detection, and so on. There are many classification algorithms available. Naïve Bayes remains one of the oldest and most popular classifiers. On one hand, implementation of naïve Bayes is simple and, on the other hand, this also requires fewer amounts of training data. From the literature review, it is found that naïve Bayes performs poorly compared to other classifiers in text classification. As a result, this makes the naïve Bayes classifier unusable in spite of the simplicity and intuitiveness of the model. In this paper, we propose a two-step feature selection method based on firstly a univariate feature selection and then feature clustering, where we use the univariate feature selection method to reduce the search space and then apply clustering to select relatively independent feature sets. We demonstrate the effectiveness of our method by a thorough evaluation and comparison over 13 datasets. The performance improvement thus achieved makes naïve Bayes comparable or superior to other classifiers. The proposed algorithm is shown to outperform other traditional methods like greedy search based wrapper or CFS.