Binary Discriminant Analysis Research Articles

Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares.

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients' lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems.

An Improved Binary Quadratic Discriminant Analysis Classifier by Using Robust Regularization

An Improved Binary Quadratic Discriminant Analysis Classifier by Using Robust Regularization

The Fidelity of Rheumatoid Arthritis Multivariate Diagnostic Biomarkers Using Discriminant Analysis and Binary Logistic Regression.

Rheumatoid arthritis (RA) is an autoimmune inflammatory disease that causes multi-articular synovitis. The illness is characterized by worsening inflammatory synovitis, which causes joint swelling and pain. Synovitis erodes articular cartilage and marginal bone, resulting in joint deterioration. This bone injury is expected to be permanent. Cytokines play a prominent role in the etiology of RA and could be useful as early diagnostic biomarkers. This research was carried out at Riyadh's King Khalid University Hospital (KKUH). Patients were enrolled from the Rheumatology unit. Seventy-eight RA patients were recruited (67 (85.9%) females and 11 (14.1%) males). Patients were selected for participation by convenience sampling. Demographic data were collected, and disease activity measurements at 28 joints were recorded using the disease activity score (DAS-28). Age- and sex-matched controls from the general population were included in the study. A panel of 27 cytokines, chemokines, and growth factors was determined in patient and control sera. Binary logistic regression (BLR) and discriminant analysis (DA) were used to analyze the data. We show that multiple cytokine biomarker profiles successfully distinguished RA patients from healthy controls. IL-17, IL-4, and RANTES were among the most predictive variables and were the only biomarkers incorporated into both BLR and DA predictive models for pooled participants (men and women). In the women-only models, the significant cytokines incorporated in the model were IL-4, IL-17, MIP-1b, and RANTES for the BLR model and IL-4, IL-1Ra, GM-CSF, IL-17, and eotaxin for the DA model. The BLR and DA men-only models contained one cytokine each, eotaxin for BLR and platelet-derived growth factor-bb (PDGF-BB) for DA. We show that BLR has a higher fidelity in identifying RA patients than DA. We also found that the use of gender-specific models marginally improves detection fidelity, indicating a possible benefit in clinical diagnosis. More research is needed to determine whether this conclusion will hold true in various and larger patient populations.

A methodological comparison of discriminant function analysis and binary logistic regression for estimating sex in forensic research and case-work.

The purpose of this study is to assess the accuracy of two multivariate statistical approaches for estimating sex from human external ear anthropometry, namely, discriminant function analysis (DFA) and binary logistic regression (BLR). A cross-sectional sample of 497 participants (233 males and 264 females) aged 18-35 years (24.42 ± 5.17) was obtained from Himachal Pradesh state of North India. Both the ears of the participants (994) were examined for anthropometric measurements. A total of 12 anthropometric measurements were taken independently on the left and right ear of each individual with the help of a pair of sliding calipers using a standard method. The sex of the population groups was discriminated against using binary logistic regression and discriminant function analysis. The predictive percentage of sex estimation computed from both the models were substantially the same, that is, 76.3% from DFA and 76.2% from BLR, with nearly comparable (∼0.02) sensitivity, specificity, positive predictive value, and negative predictive values, whereas the values of correct predicted percentage were 0.1% higher in DFA than BLR. Moreover, the other comparison metrics, such as classification error, B-index, and Matthews correlation coefficient indicated that both models performed equally well. The study highlighted that if the assumptions of the statistical methods are met, both methods are equally capable of discriminating the population depending on sex. The study recommends that the discriminant function analysis and binary logistic regression may be used synonymously in forensic research and case-work pertaining to the estimation of sex and various other forensic situations.

Risk factors for hypoxic-ischemic encephalopathy or neonatal death in placental abruption

ObjectiveTo identify risk factors for moderate or severe hypoxic-ischemic encephalopathy (HIE), or neonatal death in clinical placental abruption. Material and methodsA nested case-control study within a cohort of singleton pregnancies complicated by placental abruption with a live born infant at two academic reference centers in France, from 2006 to 2019. Cases were patients who gave birth to an infant with moderate or severe HIE or death within 28 days (HIE/death group), and controls were patients whose infant did not have any of these outcomes (no-HIE group). Independent risk factors were identified by logistic regression. Binary decision tree discriminant (CART) analysis was performed to define high-risk subgroups of HIE or death. ResultsAmong 152 patients, the infants of 44 (29%) had HIE or death. Out-of-hospital placental abruption and fetal bradycardia at admission were more frequent in cases than in controls: 39 (89%) vs 61 (56%), p < .01 and 24 (59%) vs 19 (18%), p < .01, respectively. In multivariate analysis, out-of-hospital placental abruption (aOR, 7.05; 95% CI, 1.94-25.66) and bradycardia at admission (aOR, 8.60; 95% CI, 2.51-29.42) were independently associated with an increased risk of HIE or death. The combination of out-of-hospital placental abruption and bradycardia was the highest risk situation associated with HIE or death (67%). The decision-to-delivery interval was 15 [12-20] minutes among cases. ConclusionOut-of-hospital placental abruption combined with bradycardia at admission was associated with a major risk of moderate or severe HIE or death. An optimal decision-to-delivery interval does not guarantee the absence of an adverse neonatal outcome.

Comparison of Bayesian, Frequentist and Machine learning models for predicting the two-year mortality of patients diagnosed with squamous cell carcinoma of the oral cavity

BackgroundStatistical models developed in frequentist and Bayesian context along with machine learning algorithms can encompass the multifactorial effect of the prognostic factors in predicting the outcome. This paper is aimed to compare the effect estimates and predictive performance of Bayesian, frequentist and machine learning algorithm in predicting the two-year mortality of patients diagnosed with squamous cell carcinoma (SCC) of oral cavity. Materials and methodsLogistic regression (LR), Binary Discriminant analysis (BDA), Naïve Bayes (NB), Bayesian regression (BLR), K nearest neighbor (KNN), Artificial neural network (ANN) and Random Forest (RF) models were built. The effect estimate of each prognostic factor was estimated and compared by LR and BLR model. 10-fold cross-validation was performed for internal validation of the models. The predictive performance of the models was assessed and compared. ResultsBLR model had lower and narrower effect estimates in comparison to LR model. Age and smoking are the biggest prognostic risk factors for SCC whereas surgery had the best response amongst the mode of treatment. Random forest had an AUROC of 0.86 (0.82, 0.90) whereas it was estimated to be 0.77 (0.71, 0.82) for both BLR and LR models. ConclusionsBLR model had better precision in estimating the effect size of prognostic factors and can be an alternative for predicting mortality in patients with SCC of oral cavity. Machine learning classifiers had the best predictive ability as compared to statistical models.

Early detection of colorectal cancer based on circular DNA and common clinical detection indicators.

BACKGROUNDColorectal cancer (CRC) is the third most common cancer worldwide, and it is the second leading cause of death from cancer in the world, accounting for approximately 9% of all cancer deaths. Early detection of CRC is urgently needed in clinical practice.AIMTo build a multi-parameter diagnostic model for early detection of CRC. METHODSTotal 59 colorectal polyps (CRP) groups, and 101 CRC patients (38 early-stage CRC and 63 advanced CRC) for model establishment. In addition, 30 CRP groups, and 62 CRC patients (30 early-stage CRC and 32 advanced CRC) were separately included to validate the model. 51 commonly used clinical detection indicators and the 4 extrachromosomal circular DNA markers NDUFB7, CAMK1D, PIK3CD and PSEN2 that we screened earlier. Four multi-parameter joint analysis methods: binary logistic regression analysis, discriminant analysis, classification tree and neural network to establish a multi-parameter joint diagnosis model. RESULTSNeural network included carcinoembryonic antigen (CEA), ischemia-modified albumin (IMA), sialic acid (SA), PIK3CD and lipoprotein a (LPa) was chosen as the optimal multi-parameter combined auxiliary diagnosis model to distinguish CRP and CRC group, when it differentiated 59 CRP and 101 CRC, its overall accuracy was 90.8%, its area under the curve (AUC) was 0.959 (0.934, 0.985), and the sensitivity and specificity were 91.5% and 82.2%, respectively. After validation, when distinguishing based on 30 CRP and 62 CRC patients, the AUC was 0.965 (0.930-1.000), and its sensitivity and specificity were 66.1% and 70.0%. When distinguishing based on 30 CRP and 32 early-stage CRC patients, the AUC was 0.960 (0.916-1.000), with a sensitivity and specificity of 87.5% and 90.0%, distinguishing based on 30 CRP and 30 advanced CRC patients, the AUC was 0.970 (0.936-1.000), with a sensitivity and specificity of 96.7% and 86.7%.CONCLUSIONWe built a multi-parameter neural network diagnostic model included CEA, IMA, SA, PIK3CD and LPa for early detection of CRC, compared to the conventional CEA, it showed significant improvement.

Binary cross coupled discriminant analysis for visual kinship verification

Kinship verification refers to comparing similarities between two different individuals through their facial images. In this context, feature descriptors play a crucial role, and few feature descriptors are present in literature to extract kin features from facial images. In this paper, we propose a binary cross-coupled discriminant analysis (BC2DA) based feature descriptor which is able to extract effective kin features from input facial image pairs. This method reduces the discrimination between kin pairs at the feature extraction stage itself. BC2DA converts original kin image pairs to encoded image pairs to reduce the discrimination between them. To make better use of tri-subject kin relations, we further propose multi cross-coupled discriminant analysis (MC2DA). This method reduces the discrimination between child and both parents’ images at the feature extraction stage. Extensive experiments were conducted on six kinship datasets such as KinfaceW-I/II, Cornell, FIW, TSKinface UBKinface to show the efficacy of the proposed algorithm.

Classification Modelling: A Case Study of Breast Cancer Patients of Islamabad

Purpose: The rate of breast cancer in Pakistan is the highest among all other Asian countries and all other types of cancer. The foremost treatment for breast cancer patients of stage 2 and stage 3 is surgery. The main types of surgery in this era are Mastectomy and Breast Conservative surgery. The decision about the type of surgery depends on the demographic and clinical factors. Approach: In this study, the seven characteristics have been considered. A purposive sample of 365 breast cancer patients were collected from the two main hospitals in Islamabad. The foremost objective of this study was to classify each breast cancer patient regarding surgery type based on significant explanatory characteristics. The binary logistics regression and discriminant analysis techniques were used and the significance of each parameter was tested. Findings: The main effects i.e., age, tumor size, Estrogen Receptor, and Progesterone Receptor were found to be significant with some diverse probabilities and all two-factor interactions were found to be non-significant. The sensitivity of logistic regression and discriminant analysis is almost the same i.e., 93.1% and 92.8% respectively whereas the specificity of these two techniques is also almost the same i.e., 70.8% and 71.9% respectively. The overall actual correct classify rate and Apparent error rate of both these techniques are found to be 87.7% and 12.3% respectively. Implications: In brief, it was deducted that the Tumor size stage is the most imperative characteristic among other significant characteristics in discriminating between two types of surgery

ANALISIS KLASIFIKASI ARTIST MUSIC MENGGUNAKAN MODEL REGRESI LOGISTIK BINER DAN ANALISIS DISKRIMINAN

Characteristics of a song are an important aspect that must be kept authentic by a singer. Using the Spotify API feature, we can extract the characteristics or elements of a song sung by a singer. There are eight (8) elements that we can get from the extraction of a song, namely: Danceability, Energy, Loudness, Speechiness, Acousticness, Liveness, Valence, and Tempo. Based on the extraction results, we can label the music artist using the classification analysis method. In this study, the labels are music artists, namely Ariana Grande and Taylor Swift. This study aims to obtain the classification of music artist labels using binary logistic regression methods and discriminant analysis. The response variable used in this study is Artist Music (Y) which is categorized into two categories, namely Ariana Grande (Y=0) and Taylor Swift (Y=1). The data will be divided into training and testing data with the proportion of data 90:10 and 80:20. Based on the results of the analysis, the binary regression model that was built, with the proportion of training testing data that is 90:10 has a classification accuracy for data testing of 90.00%.

An efficient approach for discriminant analysis based on adaptive feature augmentation

Effective discriminant analysis is of great practical importance, as demonstrated by economic and genetic applications. Feature augmentation via nonparametric and selection (FANS) is an efficient approach that has been widely used in classification. However, FANS may impair efficiency when a linear decision boundary separates data reasonably well. The available remedy is to use both the transformed features and original ones, which may increase computational cost and model complexity. Motivated by these concerns, this paper proposes an efficient nonparametric approach for binary discriminant analysis, called adaptive FANS, integrating augmentation and nonparametric tests. In this procedure, the original features or transformed ones are used selectively to keep the number of features constant. Thus, this procedure avoids the ergodic transformation and reduces error caused by nonparametric estimation and computational complexity. Simulation and real data analysis demonstrate its competitiveness and significant adaptability. Moreover, our approach can be easily extended to other linear frameworks.

Forecasting model for disease risk period in chickpea x collar rot pathosystem

Collar rot caused by Sclerotium rolfsii Sacc. is one of the major biotic constraints of chickpea production worldwide. It is soil-borne fungi having wider host range and infection mainly occurs at the juvenile stage of crop growth resulting crop failure in no time. The pathogen is greatly influenced by soil temperature (ST) and soil moisture (SM) therefore, experiment formulated to develop a suitable forecasting model for its future use in computer simulation of plant disease prognostication by feeding only soil temperature and moisture data. The popular desi type chickpea variety Anuradha sown at different dates to get a range of soil temperature and soil moisture combination and its corresponding effect on disease incidence was recorded under natural epiphytotic conditions. The data obtained were analyzed using binary logistic regression and discriminant analysis to assess disease risk and non-risk period. The model developed was Y'= -73.9 + 1.251 SM + 0.017 ST. The outcome recorded, a unique statistically significant contribution of soil moisture (p value=0.029) on the establishment of the disease whereas, the effect of soil temperature was detected as statistically non-significant. The model developed and the correctness of the model determined to predict the disease severity with 80 % accuracy.

Balanced scorecard usage and organizational effectiveness: evidence from manufacturing sector

PurposeThis study aims to investigate the drivers and impact of balanced scorecard (BSC) usage on organizational effectiveness in manufacturing companies. The objectives of the paper were to assess the organizational factors affecting the usage intensity of the BSC; the relative benefits of BSC determining its adoption speed; and the extent to which BSC usage enhances organizational effectiveness.Design/methodology/approachThe study adopted a survey research design. Data collection was through a structured questionnaire administered on senior accounting/ finance personnel of 300 manufacturing companies that are members of the Manufacturers Association of Nigeria. Binary logistic regression analysis, discriminant analysis and structural equation modeling (maximum likelihood estimation method) were used to analyze survey data obtained from 104 BSC adopters.FindingsResult shows that the three organizational factors affecting BSC usage intensity are affiliation to a foreign entity, availability of specialist skills and business strategy (strategic pattern). The strongest predictor is, however, the availability of specialist skills. The strongest determinants of the BSC adoption speed are the need for financial stability and the importance of customer feedbacks. The impact of BSC usage on organizational effectiveness is positive, statistically significant but weak. The inability of BSC usage to contribute appreciably to organizational effectiveness is attributable to the lack of integration among the performance measures in the BSC framework and the shallow usage rate of BSC.Practical implicationsAlthough it is commendable that financial stability and customer satisfaction strongly drive BSC adoption speed, the low rating recorded by other factors related to product development, employee development and process improvement suggests that the performance measures in the BSC framework are not used in an integrative manner. This also confirms that the BSC, like other innovative management accounting techniques, is applied at a rudimentary level by organizations in Nigeria.Originality/valueThe current study contributes to knowledge by exposing the organizational factors and relative benefits driving BSC adoption. It provides empirical evidence on why the BSC may not deliver the optimal benefit of improving organizational effectiveness despite its popularity and potential as an integrated performance measurement (IPM) apparatus that can add value to organizations. The paper adds to the scarce literature on IPM in developing countries. Drawing from the result that availability of specialist skills is the strongest predictor of BSC usage intensity, the practice of enmeshing the management accounting function with general accounting/finance should be discouraged.

Influence of weather factors, soil temperature and moisture on collar rot disease of betel vine (Piper betle L.) under coastal saline zone of West Bengal

A study was conducted to find out the influence of weather factors, soil temperature and soil moisture on the incidence of Sclerotium rolfsii Sacc. induced collar rot disease in betelvine (Piper betle L.), during 2016 to 2018. Fourteen soil and weather factors, taken from the agrometeorological observatory located at instructional farm of Ramkrishna Ashram Krishi Vigyan Kendra, Nimpith and recorded from a nearby betelvine boroj, were subjected to multiple regression, binary logistic regression and canonical discriminant analysis to develop a suitable disease forewarning model. The binary logistic model, Y(0/1) = 5.899 + 0.865 (Tmax) – 0.569 (SM) + 0.097 (BRHmin) was able to predict the disease risk with 78 per cent accuracy and correctly classified 94 per cent of cases during model validation in 2018. The weekly averages of maximum temperature (Tmax), soil moisture (SM) and minimum relative humidity inside the boroj (BRHmin) were found to be the most significant predictors of disease incidence, in this model. The soil moisture at 69 - 72 per cent of field capacity, minimum temperature of 25 - 27oC, maximum temperature of 33 - 36oC, average soil temperature of 28 - 30oC, minimum relative humidity of 60 - 72 per cent inside the boroj and maximum relative humidity of 83 - 89 per cent inside the boroj were found to be highly congenial for collar rot disease incidence in betelvine under coastal saline zone of West Bengal.

Alcohol and tobacco influencing prevalence of hypertension among 15–54 years old Indian men: An application of discriminant analysis using National Family Health Survey (NFHS), 2015–16

BackgroundHypertension has been described as an “iceberg disease” as those who suffer are usually unaware and hence usually seek healthcare services at a very late stage. It is estimated that more than two million Indians are suffering from hypertensive heart diseases that contributed to above 0.13 million deaths in 2016. ObjectivesThe present study aims to know the prevalence of Hypertension in India and its variation by socioeconomic backgrounds, and to find out risk factors discriminating hypertension with special emphasis on tobacco and alcohol consumption among men aged 15–54 years in India. MethodsIn this study, NFHS (2015–16) data were analyzed by applying binary logistic regression and discriminant analysis in the attempt to find significant predictors and discriminants of interest. ResultsThe prevalence of hypertension was 16.5% in the study population. The results suggested that the consumption of alcohol and tobacco are significant discriminant characteristics associated with hypertension irrespective of the socioeconomic background characteristics of the respondents. Hypertension is a chronic disease and is itself a causative factor for several other diseases like renal impairment, other cardiovascular diseases, and stroke. ConclusionsThe findings are expected to have direct implications on public health interventions and policy formulation to reduce diseases caused by tobacco and alcohol, particularly hypertension among men in the potential economically productive age group in India and similar settings.

Insider Threat Detection and Prevention Protocol: ITDP

Insider threat is a severe problem of many computer department since they have an authorized to do some assigned tasks. They can easily seek for any organizational computer security vulnerability. Protocol “Insider threat detection and prevention Protocol: ITDP” is designed to detect if a requesting “IT user” is authentic IT user whom has been allocated rights to particular application. User’s knowledge and his behavior are used to classify whether he is a real identified IT user or not. Statistical classification technique is used to predict if the guest is authentic. Best classification technique is linear binary discriminant function analysis with 98.3 percentage of accuracy in insider threat detection classification.

Binary classification on compositional data

Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.

Comparative study of Image Classification Algorithms

This investigation analyzed five common machine learning techniques for performing image classification included Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Binary Decision Tree (BDT) and Discriminant Analysis (DA). AlexNet deep learning model was utilized to fabricate these machine learning classifiers. The structure classifiers were executed and assessed by standard execution models of Accuracy (ACC), Precision (P), Sensitivity (S), Specificity (Spe) and Area Under the ROC Curve (AUC). The five strategies were assessed utilizing 2608 histopathological pictures for head and neck cancer. The examination was directed utilizing multiple times 10-overlay cross validation. For every strategy, the pre-trained AlexNet network was utilized to separate highlights from the activation layer. The outcomes outlined that, there was no contrast between the consequences of SVM and KNN. Both have the equivalent and the higher accuracy than others were 99.98 %, though 99.81%, 97.32% and 93.68% for DA, BDT and NB, separately. The current examination shows that the SVM, KNN and DA are the best techniques for classifying our dataset images.

A comparison between binary logistic regression and discriminant analysis in determining the most important factors affecting the instability of marital life in the Kingdom of Saudi Arabia

A comparison between binary logistic regression and discriminant analysis in determining the most important factors affecting the instability of marital life in the Kingdom of Saudi Arabia

Establishing Objective Measures of Clinical Competence in Undergraduate Medical Education Through Immersive Virtual Reality

ObjectiveThe Association of American Medical Colleges defines recognition of the need for urgent or emergent escalation of care as a key Entrustable Professional Activity (EPA) for entering residency (EPA#10). This study pilots the use of an immersive virtual reality (VR) platform for defining objective observable behaviors as standards for evaluation of medical student recognition of impending respiratory failure.MethodsA cross-sectional observational study was conducted from July 2018 to December 2019, evaluating student performance during a VR scenario of an infant in impending respiratory failure using the OculusRift VR platform. Video recordings were rated by 2 pair of physician reviewers blinded to student identity. One pair provided a consensus global assessment of performance (not competent, borderline, or competent) while the other used a checklist of observable behaviors to rate performance. Binary discriminant analysis was used to identify the observable behaviors that predicted the global assessment rating.ResultsTwenty-six fourth year medical students participated. Student performance of 8 observable behaviors was found to be most predictive of a rating of competent, with a 91% probability. Correctly stating that the patient required an escalation of care had the largest contribution toward predicting a rating of competent, followed by commenting on the patient's increased heart rate, low oxygen saturation, increased respiratory rate, and stating that the patient was in respiratory distress.ConclusionsThis study demonstrates that VR can be used to establish objective and observable performance standards for assessment of EPA attainment – a key step in moving towards competency based medical education.