Quadratic Discriminant Analysis Classifier Research Articles

An Intelligent Model for Predictions of PIWI-Interacting RNAs and Their Functions

A documented class of short non-coding RNA molecules is known as PIWI interacting RNA (PiRNA). The creation of new drugs and the identification of various tumor types are linked to the PiRNA molecules. Additionally, it is related to controlling transcription of genes, squelching transposons, and preserving genomic stability. The discovery of piRNAs and their functionality has grown to be a significant research topic in bioinformatics because of the crucial influence that piRNAs play in biological processes. The 2L-piRNA-ML predictor is a strong two-layer predictor that is suggested in this research to enhance the prediction of PiRNA and their functionality. The suggested model uses Quadratic Discriminant Analysis Classifier, Linear Discriminant Analysis, Passive Aggressive Classifier, Extra Tree Classifier, Logistic Regression, Random Forest, Ridge Classifier CV for classification. It also employs DNC and TNC for extraction of features. The suggested model is created using a two-layer construction strategy. The 1st layer makes a prediction about a given sequence whether it is PiRNA or not, and the 2nd layer makes a prediction about a given PiRNA sequence whether it is having the function of instructing target mRNA deadenylation or not. Proposed model achieved 95.65 % accuracy at the first layer and 92.30% accuracy at the second layer.

Energy efficient FPGA implementation of an epileptic seizure detection system using a QDA classifier

Epilepsy is a severe neurological disorder that causes seizures. It is detected by analyzing the electrical impulses of the human brain. Monitoring the brain is commonly done using an electroencephalogram (EEG). Seizure detection from the large recorded EEG dataset is a demanding task. However, numerous machine learning classifiers and the appropriate features can detect seizures. The Hjorth and statistical parameters are used in this study to provide an effective method for field programmable gate array (FPGA) realization of epileptic seizure detectors from EEG signals. Mobility, interquartile range (IQR), median absolute deviation (MAD), energy, non-linear energy, and simple square integral (SSI) are the various features analyzed in this work. The hardware architecture of the seizure detection system is captured in Verilog hardware descriptive language and realized on the Artix-7 FPGA. This paper presents three separate seizure detection models designed by pairing three different features with the Quadratic discriminant analysis (QDA) classifier. Among these three models, the energy and non-linear energy-based seizure detection system offers better performance than existing seizure detection systems because it possesses the lowest dynamic power consumption (0.116 µW), the highest design accuracy (99.4 %), and the highest sensitivity (100 %), which makes it the best seizure detection system for real-time applications.

Dynamic Feature Extraction-Based Quadratic Discriminant Analysis for Industrial Process Fault Classification and Diagnosis.

This paper introduces a novel method for enhancing fault classification and diagnosis in dynamic nonlinear processes. The method focuses on dynamic feature extraction within multivariate time series data and utilizes dynamic reconstruction errors to augment the feature set. A fault classification procedure is then developed, using the weighted maximum scatter difference (WMSD) dimensionality reduction criterion and quadratic discriminant analysis (QDA) classifier. This method addresses the challenge of high-dimensional, sample-limited fault classification, offering early diagnosis capabilities for online samples with smaller amplitudes than the training set. Validation is conducted using a cold rolling mill simulation model, with performance compared to classical methods like linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD). The results demonstrate the superiority of the proposed method for reliable industrial process monitoring and fault diagnosis.

Harvest Forecasting Improvement Using Federated Learning and Ensemble Model

Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together. These presented nine algorithms achieved exemplary satisfactory accuracies. The powerful contributions of proposed algorithms can create exact harvest forecasting. Ultimately, we intend to compare our study with the earlier research's results.

Field programmable gate array‐based energy‐efficient and fast epileptic seizure detection using support vector machine and quadratic discriminant analysis classifier

AbstractEpilepsy is a serious neurological disorder that results in seizures. It can be diagnosed by analyzing the brain's electrical activity using an electroencephalogram (EEG). However, the detection of seizures from massive EEG datasets is a challenging task. To address this challenge, researchers have developed several machine‐learning classifiers and feature extraction techniques for detecting seizures. This paper proposes an energy‐efficient and fast field programmable gate array (FPGA) architecture for detecting epileptic seizures using minimal computational resources. The seizure detection system uses the one Hjorth parameter (mobility) and another statistical parameter (nonlinear energy) as features and employs two efficient classifiers, quadratic discriminant analysis and linear support vector machine (LSVM), for classifying signals into seizure and nonseizure categories. The feature extractor block is connected individually to each of these classifiers. Subsequently, the performance of these two proposed models is evaluated in terms of accuracy, sensitivity, power consumption, resource utilization, and other metrics. The results demonstrate that the SVM classifier‐based model achieved the highest accuracy (99.4%) and sensitivity (98.8%) while consuming minimal dynamic power (0.057 mW) and utilizing the minimum FPGA resources. Thus, the proposed hardware system offers a reliable and energy‐efficient solution for detecting seizures in clinical and real‐time applications.

Cropland Mapping Using Sentinel-1 Data in the Southern Part of the Russian Far East.

Crop identification is one of the most important tasks in digital farming. The use of remote sensing data makes it possible to clarify the boundaries of fields and identify fallow land. This study considered the possibility of using the seasonal variation in the Dual-polarization Radar Vegetation Index (DpRVI), which was calculated based on data acquired by the Sentinel-1B satellite between May and October 2021, as the main characteristic. Radar images of the Khabarovskiy District of the Khabarovsk Territory, as well as those of the Arkharinskiy, Ivanovskiy, and Oktyabrskiy districts in the Amur Region (Russian Far East), were obtained and processed. The identifiable classes were soybean and oat crops, as well as fallow land. Classification was carried out using the Support Vector Machines, Quadratic Discriminant Analysis (QDA), and Random Forest (RF) algorithms. The training (848 ha) and test (364 ha) samples were located in Khabarovskiy District. The best overall accuracy on the test set (82.0%) was achieved using RF. Classification accuracy at the field level was 79%. When using the QDA classifier on cropland in the Amur Region (2324 ha), the overall classification accuracy was 83.1% (F1 was 0.86 for soybean, 0.84 for fallow, and 0.79 for oat). Application of the Radar Vegetation Index (RVI) and VV/VH ratio enabled an overall classification accuracy in the Amur region of 74.9% and 74.6%, respectively. Thus, using DpRVI allowed us to achieve greater performance compared to other SAR data, and it can be used to identify crops in the south of the Far East and serve as the basis for the automatic classification of cropland.

The effect of intrinsic dimension on the Bayes-error of projected quadratic discriminant classification

High-dimensionality is a common hurdle in machine learning and pattern classification; mitigating its effects has attracted extensive research efforts. It has been found in a recent NeurIPS paper that, when the data possesses a low effective dimension, the predictive performance of a discriminative quadratic classifier with nuclear norm regularisation enjoys a reduced (logarithmic) dependence on the ambient dimension and depends on the effective dimension instead, while other regularisers are insensitive to the effective dimension. In this paper, we show that dependence on the effective dimension is also exhibited by the Bayes error of the generative Quadratic Discriminant Analysis (QDA) classifier, without any explicit regularisation, under three linear dimensionality reduction schemes. Specifically, we derive upper bounds on the Bayes error of QDA, which adapt to the effective dimension, and entirely bypass any dependence on the ambient dimension. Our findings complement previous results on compressive QDA that were obtained under compressive sensing type assumptions on the covariance structure. In contrast, our bounds make no a-priori assumptions on the covariance structure, in turn they tighten in the presence of benign traits of the covariance. We corroborate our findings with numerical experiments.

Evaluation of Stem Rust Disease in Wheat Fields by Drone Hyperspectral Imaging.

Detecting plant disease severity could help growers and researchers study how the disease impacts cereal crops to make timely decisions. Advanced technology is needed to protect cereals that feed the increasing population using fewer chemicals; this may lead to reduced labor usage and cost in the field. Accurate detection of wheat stem rust, an emerging threat to wheat production, could inform growers to make management decisions and assist plant breeders in making line selections. A hyperspectral camera mounted on an unmanned aerial vehicle (UAV) was utilized in this study to evaluate the severity of wheat stem rust disease in a disease trial containing 960 plots. Quadratic discriminant analysis (QDA) and random forest classifier (RFC), decision tree classification, and support vector machine (SVM) were applied to select the wavelengths and spectral vegetation indices (SVIs). The trial plots were divided into four levels based on ground truth disease severities: class 0 (healthy, severity 0), class 1 (mildly diseased, severity 1-15), class 2 (moderately diseased, severity 16-34), and class 3 (severely diseased, highest severity observed). The RFC method achieved the highest overall classification accuracy (85%). For the spectral vegetation indices (SVIs), the highest classification rate was recorded by RFC, and the accuracy was 76%. The Green NDVI (GNDVI), Photochemical Reflectance Index (PRI), Red-Edge Vegetation Stress Index (RVS1), and Chlorophyll Green (Chl green) were selected from 14 SVIs. In addition, binary classification of mildly diseased vs. non-diseased was also conducted using the classifiers and achieved 88% classification accuracy. This highlighted that hyperspectral imaging was sensitive enough to discriminate between low levels of stem rust disease vs. no disease. The results of this study demonstrated that drone hyperspectral imaging can discriminate stem rust disease levels so that breeders can select disease-resistant varieties more efficiently. The detection of low disease severity capability of drone hyperspectral imaging can help farmers identify early disease outbreaks and enable more timely management of their fields. Based on this study, it is also possible to build a new inexpensive multispectral sensor to diagnose wheat stem rust disease accurately.

Identification of three medically important mosquito species using Raman spectroscopy

AbstractAccurate identification of disease vectors is crucial when collecting epidemiological data. In mosquitoes, which transmit diseases like malaria, yellow fever, chikungunya, and dengue fever, identification mainly relies on the observation of external morphological features at different life cycle stages. This process is tedious and labor‐intensive. In this paper, the utility of Raman spectroscopy to discriminate and classify three mosquito species, namely,Aedes aegypti,Anopheles gambiae,andCulex quinquefasciatus,is presented. The three species were chosen to represent two subfamilies of medically important mosquitoes, that is, the Anophelinae and the Culicinae. The study is primarily a proof of concept on the potential of Raman spectroscopy in mosquito taxonomy. A dispersive Raman microscope was used to record spectra from the legs (femur and tibia) of fresh anesthetized laboratory‐bred mosquitoes. Broad peaks centered around 1400, 1590, and 2060 cm−1dominated the spectra. These peaks, attributed to cuticular melanin, were important in mosquito discrimination. Variance threshold (VT) and principal component analysis (PCA) were used for feature selection and feature extraction, respectively. The extracted features were then used to train and test linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) classifiers. VT/PCA/QDA achieved an overall accuracy of 94%, a sensitivity of 87%, and a specificity of 96%, whereas VT/PCA/LDA attained an accuracy of 85%, a sensitivity of 69%, and a specificity of 90%. The success of these relatively simple classification models on Raman spectroscopy data lays the ground for future development of machine learning models that may include discrimination of cryptic species.

Estimating propagation probability from meteorological to ecological droughts using a hybrid machine learning copula method

Abstract. The impact of droughts on vegetation is essentially manifested as the transition of water shortage from the meteorological to ecological stages. Therefore, understanding the mechanism of drought propagation from meteorological to ecological drought is crucial for ecological conservation. This study proposes a method for calculating the probability of meteorological drought to trigger ecological drought at different magnitudes in northwestern China. In this approach, meteorological and ecological drought events during 1982–2020 are identified using the three-dimensional identification method; the propagated drought events are extracted according to a certain spatiotemporal overlap rule, and propagation probability is calculated by coupling the machine learning model and C-vine copula. The results indicate that (1) 46 drought events are successfully paired with 130 meteorological and 184 ecological drought events during 1982–2020, and ecological drought exhibits a longer duration but smaller affected area and severity than meteorological drought; (2) a quadratic discriminant analysis (QDA) classifier performs the best among the 11 commonly used machine learning models which are combined with four-dimensional C-vine copula to construct the drought propagation probability model; and (3) the hybrid method considers more drought characteristics and a more detailed propagation process which addresses the limited applicability of the traditional method to regions with large spatial extent.

OCT-Derived Radiomic Features Predict Anti-VEGF Response and Durability in Neovascular Age-Related Macular Degeneration.

No established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability. Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy. Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution). A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response. The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance. The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained. Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.

Retinal Shape-Based Classification of Retinal Detachment and Posterior Vitreous Detachment Eyes.

Retinal detachment is a sight-threatening emergency, with more than half of those affected suffering permanent visual impairment. A diagnostic test to identify eyes at risk before vision is threatened would enable exploration of prophylactic treatment. This report presents the use of irregularities in retinal shape, quantified from optical coherence tomography (OCT) images, as a biomarker for retinal detachment. OCT images were taken from posterior and mid-peripheral retina of 264 individuals [97 after a posterior vitreous detachment (PVD), 99 after vitrectomy for retinal detachment and 68 after laser for a retinal tear]. Diagnoses were taken from history, examination and OCT. Retinal irregularity was quantified in the frequency domain, and the distribution of irregularity across the regions of the eye was explored to identify features exhibiting the greatest difference between retinal detachment and PVD eyes. Two of these features plus axial length were used to train a quadratic discriminant analysis classifier. Classifier performance was assessed by its sensitivity and specificity in identifying retinal detachment eyes and visualised with a receiver operating characteristic (ROC) curve. Validation set specificity was 84% (44/52 PVD eyes correctly labelled) and sensitivity 35% (23/64 retinal detachment eyes identified, p = 0.02). Area under the ROC curve was 0.75 (95% confidence intervals 0.58-0.85). Retinal detachment eyes were significantly more irregular than PVD eyes in the superior retina (0.70mm versus 0.49mm, p < 0.05) and supero-temporal retina (1.12mm versus 0.80mm, p < 0.05). Lower sensitivity (16/68, 24%) was seen for eyes with a retinal tear without detachment, that were intermediate in size between retinal detachment and PVD eyes. Axial length on its own was a poor classifier. Neither irregularity nor classification were affected by surgery for retinal detachment or the development of PVD. The classifier identified 1/3 of retinal detachment eyes in this sample. In future work, these features can be evaluated as a test for retinal detachment prior to PVD.

Trainable segmentation for transmission electron microscope images of inorganic nanoparticles.

We present a trainable segmentation method implemented within the python package ParticleSpy. The method takes user labelled pixels, which are used to train a classifier and segment images of inorganic nanoparticles from transmission electron microscope images. This implementation is based on the trainable Waikato Environment for Knowledge Analysis (WEKA) segmentation, but is written in python, allowing a large degree of flexibility and meaning it can be easily expanded using other python packages. We find that trainable segmentation offers better accuracy than global or local thresholding methods and requires as few as 100 user-labelled pixels to produce an accurate segmentation. Trainable segmentation presents a balance of accuracy and training time between global/local thresholding and neural networks, when used on transmission electron microscope images of nanoparticles. We also quantitatively investigate the effectiveness of the components of trainable segmentation, its filter kernels and classifiers, in order to demonstrate the use cases for the different filter kernels in ParticleSpy and the most accurate classifiers for different data types. A set of filter kernels is identified that are effective in distinguishing particles from background but that retain dissimilar features. In terms of classifiers, we find that different classifiers perform optimally for different image contrast; specifically, a random forest classifier performs best for high-contrast ADF images, but that QDA and Gaussian Naïve Bayes classifiers perform better for low-contrast TEM images.

Toward smart manufacturing: Analysis and classification of cutting parameters and energy consumption patterns in turning processes

Advanced monitoring technologies with embedded devices, sensors, and wireless data communication have been developed to capture machine, process, tool dconditions, and energy consumption and become essential to guarantee industrial intelligence in modern manufacturing. This paper studies the correlation of turning process parameters of a CNC machine with the tool vibration, acoustic signal, and energy consumption using preliminary data analysis and machine learning methods. A turning process is being monitored through three different sensors, namely vibration, acoustic and current. A vibration sensor was attached to the tool to measure the variation in tool vibration with respect to machining conditions. Additionally, a microphone was attached to the tool holder to capture the acoustic signature of the process. Further, current clamps were attached to the power leads going into the machine’s main circuit breaker, allowing to monitor the power consumption during operation. Machining parameters consisted of three different spindle speeds, three feed rates, and the depth of cut was kept constant throughout all experiments. Signals from each sensor were independently analyzed to examine the effect of process parameters on the change of vibration, acoustic signature, and power consumption. Additionally, low-cost machine learning models were applied to classify the cutting conditions using the measured acoustic signals as training, validation, and test samples. It was found that vibration signals are more sensitive to the change in spindle speed while power response is more sensitive to the change in feed rates. On the other hand, speeds and feeds have combined effects on the acoustic signal. Finally, the low-cost machine learning model using the Quadratic Discriminant Analysis (QDA) classifier could correctly correlate the acoustic signals to nine different operating conditions with an accuracy of 86.9%

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-time detection of COVID-19 using reverse transcription polymerase chain reaction (RT-PCR) test data, such as limited datasets, imbalance classes, a high misclassification rate of models, and the need for specialized research in identifying the best features and thus improving prediction rates. This study aims to investigate and apply the ensemble learning approach to develop prediction models for effective detection of COVID-19 using routine laboratory blood test results. Hence, an ensemble machine learning-based COVID-19 detection system is presented, aiming to aid clinicians to diagnose this virus effectively. The experiment was conducted using custom convolutional neural network (CNN) models as a first-stage classifier and 15 supervised machine learning algorithms as a second-stage classifier: K-Nearest Neighbors, Support Vector Machine (Linear and RBF), Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, AdaBoost, ExtraTrees, Logistic Regression, Linear and Quadratic Discriminant Analysis (LDA/QDA), Passive, Ridge, and Stochastic Gradient Descent Classifier. Our findings show that an ensemble learning model based on DNN and ExtraTrees achieved a mean accuracy of 99.28% and area under curve (AUC) of 99.4%, while AdaBoost gave a mean accuracy of 99.28% and AUC of 98.8% on the San Raffaele Hospital dataset, respectively. The comparison of the proposed COVID-19 detection approach with other state-of-the-art approaches using the same dataset shows that the proposed method outperforms several other COVID-19 diagnostics methods.

Obstructive Sleep Apnea Detection Using Speech Signals with High Frequency Components

In this study, the Obstructive Sleep Apnea (OSA) detection using speech signals during awake is considered. Traditional speech based OSA detection methods adopt traditional features (Formants, MFCC, etc.) on normal speech frequency range (&lt;6kHz). However, it ignores the signal components outside this range that usually appear in pathological voices. In this paper, higher order traditional speech features (with more high frequency components) are adopted for detection. To better characterize OSA patients’ speech, a high frequency feature set is proposed. It consists of the traditional speech features with optimized parameters and a new proposed feature: High frequency energy. Principal Component Analysis (PCA) based Sequence Forward Feature Selection (PCASFFS) are adopted as feature selection. In the simulation using 66 OSA patients’ speech signals, it achieves an accuracy of 84.85% for multi-class (4 levels) detection with the proposed high frequency feature set using quadratic discriminant analysis classifier (QDA).

The Wavelia Microwave Breast Imaging system-tumour discriminating features and their clinical usefulness.

Objective:The Wavelia Microwave Breast Imaging (MBI) system, based on non-ionising imaging technology, has demonstrated exciting potential in the detection and localisation of breast pathology in symptomatic patients. In this study, the ability of the system to accurately estimate the size and likelihood of malignancy of breast lesions is detailed, and its clinical usefulness determined.Methods:Institutional review board and Health Products Regulatory Authority (HPRA) approval were obtained. Patients were recruited from the symptomatic unit to three groups; breast cancer (Group-1), unaspirated cysts (Group-2) and biopsied benign lesions (Group-3). MBI, radiological and histopathological findings were reviewed. MBI size estimations were compared with the sizes determined by conventional imaging and histopathology. A Quadratic Discriminant Analysis (QDA) classifier was trained in a 3D feature space to discriminate malignant from benign lesions. An independent review was performed by two independent breast radiologists.Results:24 patients (11 Group-1, 8 Group-2 and 5 Group-3) underwent MBI. The Wavelia system was more accurate than conventional imaging in size estimation of breast cancers. The QDA accurately separated benign from malignant breast lesions in 88.5% of cases. The addition of MBI and the Wavelia malignancy risk calculation was deemed useful by the two radiologists in 70.6% of cases.Conclusion:The results from this MBI investigation demonstrate the potential of this novel system in estimating size and malignancy risk of breast lesions. This system holds significant promise as a potential non-invasive, comfortable, and harmless adjunct for breast cancer diagnosis. Further larger studies are under preparation to validate the findings of this study.Advances in knowledge:This study details the potential of the Wavelia MBI system in delineating size and malignancy risk of benign and malignant breast lesions in a symptomatic cohort. The usefulness of the Wavelia system is assessed in the clinical setting.

Gaussian process-based kernel as a diagnostic model for prediction of type 2 diabetes mellitus risk using non-linear heart rate variability features

The main objective of the study was to develop a low-cost, non-invasive diagnostic model for the early prediction of T2DM risk and validation of this model on patients. The model was designed based on the machine learning classification technique using non-linear Heart rate variability (HRV) features. The electrocardiogram of the healthy subjects (n = 35) and T2DM subjects (n = 100) were recorded in the supine position for 15min, and HRV features were extracted. The significant non-linear HRV features were identified through statistical analysis. It was found that Poincare plot features (SD1 and SD2) can differentiate the T2DM subject data from healthy subject data. Several machine learning classifiers, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis, Naïve Bayes, and Gaussian Process Classifier (GPC), have classified the data based on the cross-validation approach. A GP classifier was implemented using three kernels, namely radial basis, linear, and polynomial kernel, considering the ability to handle the non-linear data. The classifier performance was evaluated and compared using performance metrics such as accuracy(AC), sensitivity(SN), specificity(SP), precision(PR), F1 score, and area under the receiver operating characteristic curve(AUC). Initially, all non-linear HRV features were selected for classification, but the specificity of the model was the limitation. Thus, only two Poincare plot features were used to design the diagnostic model. Our diagnostic model shows the performance using GPC based linear kernel as AC of 92.59%, SN of 96.07%, SP of 81.81%, PR of 94.23%, F1 score of 0.95, and AUC of 0.89, which are more extensive compared to other classification models. Further, the diagnostic model was deployed on the hardware module. Its performance on unknown/test data was validated on 65 subjects (healthy n = 15 and T2DM n = 50). Considering the desirable performance of the diagnostic model, it can be used as an initial screening test tool for a healthcare practitioner to predict T2DM risk.

Comparative Analysis of Texture Patterns on Mammograms for Classification

Breast cancer is a cancerous tumor that arrives within the tissues of the breast. Women are mostly attacked than men. To detect early cancer medical specialists, suggest mammography for screening. Algorithms in Machine learning were executed on mammogram images to classify whether the tissues are deleterious or not. An analysis is done based on the texture feature extraction using different techniques like Frequency decoded local binary pattern (FDLBP), Local Bit-plane Decoded Pattern (LBDP), Local Diagonal Extrema Pattern (LDEP), Local Directional Order Pattern (LDOP), Local Wavelet Pattern (LWP). The features extracted are tested on 322 images from MIA’s database of three different classes. The algorithms in Machine learning like K-Nearest Neighbor classifier (KNN), Support vector classifier (SVC), Decision Tree classifier (DTC), Random Forest classifier (RFC), AdaBoost classifier (AC), Gradient Boosting classifier (GBC), Gaussian Naive Bayes classifier (GNB), Linear Discriminant Analysis classifier (LDA), Quadratic Discriminant Analysis classifier (QDA) were used to evaluate the accuracy of classification. This paper examines the comparison of accuracy using different texture features. KNN algorithm with LDEP for texture feature extraction gives classification accuracy of 64.61%, SVC with all the texture patterns mentioned gives classification accuracy of 63.07%, DTC with FDLBP, LBDP gives classification accuracy of 47.69, RFC with LBDP and AC with LDOP and GBC with FDLBP gives 61.53% classification accuracy, GNB and LDA with FDLBP gives 60% and 63.07% classification accuracy respectively, QDA with LBDP gives 64.61 classification accuracy. Of all the Algorithms support vector classifier gives good accuracy results with all the texture patterns mentioned.

Robust generalised quadratic discriminant analysis

Quadratic discriminant analysis (QDA) is a widely used statistical tool to classify observations from different multivariate Normal populations. The generalized quadratic discriminant analysis (GQDA) classification rule/classifier, which generalizes the QDA and the minimum Mahalanobis distance (MMD) classifiers to discriminate between populations with underlying elliptically symmetric distributions competes quite favorably with the QDA classifier when it is optimal and performs much better when QDA fails under non-Normal underlying distributions with heavy tail, e.g. Cauchy distribution. However, the classification rule in GQDA is still based on the sample mean vector and the sample dispersion matrix of a training set, which are extremely non-robust under data contamination. In real world, however, it is quite common to face data which are highly vulnerable to outliers and so the lack of robustness of the classical estimators of the mean vector and the dispersion matrix reduces the efficiency of the GQDA classifier significantly, increasing the misclassification errors. The present paper investigates the performance of the GQDA classifier when the classical estimators of the mean vector and the dispersion matrix used therein are replaced by various robust counterparts. Applications to various real data sets as well as simulation studies reveal far better performance of the proposed robust versions of the GQDA classifier. A comparative study has been made to advocate the appropriate choice of the robust estimators to be used in a specific situation.