Sustainable development and health assessment model of higher education in India: A mathematical modeling approach.

The Coronavirus Disease 2019 has resulted in a transition from physical education to online learning, leading to a collapse of the established educational order and a wisdom test for the education governance system. As a country seriously affected by the pandemic, the health of the Indian higher education system urgently requires assessment to achieve sustainable development and maximize educational externalities. This research systematically proposes a health assessment model from four perspectives, including educational volume, efficiency, equality, and sustainability, by employing the Technique for Order Preference by Similarity to an Ideal Solution Model, Principal Component Analysis, DEA-Tobit Model, and Augmented Solow Model. Empirical results demonstrate that India has high efficiency and an absolute health score in the higher education system through multiple comparisons between India and the other selected countries while having certain deficiencies in equality and sustainability. Additionally, single-target and multiple-target path are simultaneously proposed to enhance the Indian current education system. The multiple-target approach of the India-China-Japan-Europe-USA process is more feasible to achieve sustainable development, which would improve the overall health score from .351 to .716. This finding also reveals that the changes are relatively complex and would take 91.5 years considering the relationship between economic growth rates and crucial indicators. Four targeted policies are suggested for each catching-up period, including expanding and increasing the social funding sources, striving for government expenditure support to improve infrastructures, imposing gender equality in education, and accelerating the construction of high-quality teachers.

AMMI and GGE biplot analysis for yield performance and stability assessment of selected Bambara groundnut (Vigna subterranea L. Verdc.) genotypes under the multi-environmental trails (METs).

The stability and high yielding of Vigna subterranea L. Verdc. genotype is an important factor for long-term development and food security. The effects of G × E interaction on yield stability in 30 Bambara groundnut genotypes in four different Malaysian environments were investigated in this research. The experiment used a randomized complete block design with three replications in each environment. Over multiple harvests, yield component traits such as the total number of pods per plant, fresh pods weight (g), hundred seeds weight (g), and yield per hectare were evaluated in the main and off-season in 2020 and 2021. Stability tests for multivariate stability parameters were performed based on analyses of variance. For all the traits, the pooled analysis of variance revealed highly significant (p < 0.01) variations between genotypes, locations, seasons, and genotypes by environment (G × E interaction). A two-dimensional GGE biplot was generated using the first two principal components (axis 1 and axis 2), which accounted for 94.97% and 3.11% difference in GEI for yield per hectare, respectively. Season and location were found to be the most significant causes of yield heterogeneity, accounting for 31.13% and 14.02% of overall G + E + G × E variation, respectively, according to the combined study of variance. The GGE biplot revealed that the three winning genotypes G1, G3, and G5 appear across environments whereas AMMI model exposed genotypes viz G18, G14, G7, G3, G1, and G5 as best performer. Based on ideal genotype ranking genotype G1 was the best performer, with a high mean yield and high stability in the tested environment. According to the AEC line, genotypes G1 and G3 were extremely stable, while genotypes G2 and G4 were low stable, with a high average yielding per hectare. A GGE and AMMI biplot graphically showed the interrelationships between the tested environment and genotypes, classified genotypes into three categories as well as simplifying visual evaluations, according to this investigation. According to our results, breeding could improve yield production, and the genotypes discovered could be recommended for commercial cultivation.

Construction of Decision Trees Based on Gene Expression Omnibus Data to Classify Bladder Cancer and Its Subtypes.

BACKGROUND Bladder cancer is a malignant tumor of the genitourinary system. Different subtypes of bladder cancer have different treatment methods and prognoses. Therefore, identifying hub genes affecting other genes is of great significance for the treatment of bladder cancer. MATERIAL AND METHODS We obtained expression profiles from the GSE13507 and GSE77952 datasets from the Gene Expression Omnibus database. First, principal component analysis was used to identify the difference in gene expression in different types of tissues. Differential expression analysis was used to find the differentially expressed genes between normal and tumor tissues, and between tumors with and without muscle infiltration. Further, based on differentially expressed genes, we constructed 2 decision trees for differentiating between tumor and normal tissues, and between muscle-infiltrating and non-muscle-infiltrating tumor tissues. A receiver operating characteristic curve was used to evaluate the prediction effect of the decision trees. RESULTS FAM107A and C8orf4 showed significantly lower expression in bladder cancer tissues than in normal tissues. Regarding muscle infiltration, CTHRC1 showed lower expression and HMGCS2 showed higher expression in non-muscle-infiltrating samples than in those with muscle infiltration. We constructed 2 decision trees for differentiating between tumor and normal tissue, and between tissues with and without muscle infiltration. Both decision trees showed good prediction results. CONCLUSIONS These newly discovered hub genes will be helpful in understanding the occurrence and development of different subtypes of bladder cancer, and will provide new therapeutic targets and biomarkers for bladder cancer.

Assessment of the Kernel Gram Matrix Representation of Data in Order to Avoid the Alignment of Chromatographic Signals.

This article discusses the possibility of exploratory data analysis of samples described by second-order chromatographic data affected by peak shifts. In particular, the potential of the kernel Gram matrix representation as an alternative to the necessary and time-consuming alignment step is evaluated. It was demonstrated through several simulation studies and comparisons that even small peak shifts can be a substantial source of data variance, and they can easily hamper the interpretation of chromatographic data. When peak shifts are small, their negative effect is far more destructive than the impact of relatively large levels of the Gaussian noise, heteroscedastic noise, and signal's baseline. The Gram principal component analysis approach has proven to be a well-suited tool for exploratory analysis of chromatographic signals collected using the diode-array detector in which sample-to-sample peak shifts were observed.

Identification of dietary patterns by principal component analysis in schoolchildren in the South of Brazil and associated factors / Identificação de padrões alimentares por análise de componentes principais em escolares do sul do Brasil e fatores associados

Abstract Objectives: to identify dietary patterns (DP) and associated factors in first grade school-children in elementary schools in the South of Brazil. Methods: school-based cross-sectional study, with a non-probabilistic sample of 782 schoolchildren aged 6 to 8. Food intake was assessed by a food frequency questionnaire. DP were identified using the principal component analysis and the prevalence ratios were obtained by Poisson regression with a robust variance. Results: four DP were identified and accounted for 25.3% of the total variance: "fruit, vegetables and fish" (8.5%), "sweets and salty snacks" (7.0%), "dairy, ham and biscuits" (5.0%) and "common Brazilian food" (4.8%). After the adjustment, breakfast habit and lower frequency of meals in front of a screen increased the probability of adherence to a high consumption of DP of "fruit, vegetables and fish". The maternal schooling level was linearly and inversely associated with DP of "sweets and salty snacks" and "common Brazilian food", and positively related to the DP of "dairy, ham and biscuits". Schoolchildren with food inse-curity and sufficiently active had higher probability of adherence to DP of "common Brazilian food". Conclusions: four DP were identified and associated with food insecurity, maternal socioeconomic characteristics and schoolchildren's behavioral characteristics.

Resumo Objetivos: identificar padrões alimentares (PA) e fatores associados em escolares do primeiro ano do ensino fundamental de escolas municipais do sul do Brasil. Métodos: estudo transversal, de base escolar, com uma amostra não-probabilística de 782 escolares, de 6 a 8 anos. A ingestão alimentar foi avaliada por questionário de frequência alimentar. Os PA foram identificados através da análise de componentes princi-pais e razões de prevalência foram obtidas por regressão de Poisson com variância robusta. Resultados: foram identificados quatro PA que explicaram 25.3% da variância total: "frutas, verduras e peixe" (8,5%), "doces e salgadinhos" (7.0%); "laticínios, presunto e biscoitos" (5.0%) e "comum brasileiro" (4.8%). Após ajuste, hábito de realizar desjejum e baixa frequência de refeições em frente à tela aumentaram a probabilidade de adesão ao consumo elevado do PA "frutas, verduras e peixe". Escolaridade materna associou-se linear-mente e inversamente com o PA "doces e salgadinhos" e "comum brasileiro", e positiva-mente com o PA "laticínios, presunto e biscoitos". Escolares com insegurança alimentar e suficientemente ativos apresentaram maior probabilidade de adesão ao PA "comum brasileiro". Conclusões: identificaram-se quatro PA e foram observadas associações com insegu-rança alimentar, características socioeconômicas maternas e características comportamen-tais dos escolares.

Assessment of grip-motion characteristics in carpal tunnel syndrome patients using a novel finger grip dynamometer system.

BACKGROUND: Grip strength measurement is widely used in daily medical practice, and it has been reported that the grip strength decreases in patients with carpal tunnel syndrome (CTS). However, conventional grip dynamometers evaluate only the maximum power of total grip strength and cannot measure the time course of grip motion. In this report, we aimed to determine the grip characteristics of CTS patients by measuring the time course of each finger's grip motion and to analyze the relationship between finger grip strength and subjective symptoms using this new grip system. METHODS: The grip strength of each finger was measured using the new grip system that has four pressure sensors on the grip parts of each finger of the Smedley grip dynamometer. We analyzed the time course of grip motion and relationship between finger grip strength and subjective symptoms in 104 volunteer and 51 CTS hands. The Japanese Society for Surgery of the Hand version of the Carpal Tunnel Syndrome Instrument (CTSI-JSSH) and the Disability of Arm, Shoulder, and Hand questionnaire (DASH) were used as subjective evaluation scores. RESULTS: In the CTS group, the grip time with the index, middle, and ring fingers was longer, and the time at which strength was lost after reaching the maximum was earlier. Patients with severe subjective symptoms tended to not use the index and middle fingers during grip motion. CONCLUSIONS: This new system that measures each finger's grip strength at one time and record the time course of grip motion could quantify a patient's symptoms easily and objectively, which may contribute to the evaluation of hand function.

Stock price prediction using principal components.

The literature provides strong evidence that stock price values can be predicted from past price data. Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in a data set. This method is often used for dimensionality reduction and analysis of the data. In this paper, we develop a general method for stock price prediction using time-varying covariance information. To address the time-varying nature of financial time series, we assign exponential weights to the price data so that recent data points are weighted more heavily. Our proposed method involves a dimension-reduction operation constructed based on principle components. Projecting the noisy observation onto a principle subspace results in a well-conditioned problem. We illustrate our results based on historical daily price data for 150 companies from different market-capitalization categories. We compare the performance of our method to two other methods: Gauss-Bayes, which is numerically demanding, and moving average, a simple method often used by technical traders and researchers. We investigate the results based on mean squared error and directional change statistic of prediction, as measures of performance, and volatility of prediction as a measure of risk.

Assessment of volatile fingerprint by HS-SPME/GC-qMS and E-nose for the classification of cocoa bean shells using chemometrics.

The cocoa bean shell (CBS) is a main by-product of cocoa processing, with great potential to be used as an ingredient for functional foods because of its nutritional and flavour properties. This study aimed to characterise and classify CBSs obtained from cocoa beans of diverse cultivars and collected in different geographical origins through their volatile profile assessed using headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME/GC-qMS) and E-nose combined with principal component analysis (PCA). The study provides, for the first time in a representative set of samples, a comprehensive fingerprint and semi-quantitative data for >100 volatile organic compounds (VOCs), such as aldehydes, ketones, pyrazines, alcohols, and acids. Through PCA, a clear separation of the Criollo cultivar from the other cultivars was achieved with both GC-qMS and E-nose analytical techniques because of the high content of key-aroma VOCs. Several biomarkers identified by GC-qMS, such as 2-hepanol, 2-methylpropanoic acid, and 2,3,5-trimethylpyrazine, recognized as key-aroma compounds for cocoa beans, were found suitable for the classification of CBSs according to their quality and origin. GC-qMS and E-nose appeared to be suitable analytical approaches to classify CBSs, with a high correlation between both analytical techniques. The volatile fingerprint and classification of CBSs could allow for the selection of samples with a specific flavour profile according to the food application and, therefore, constitute an interesting approach to valorise this by-product as a food ingredient.

Bayesian rank penalization.

Rank minimization is a key component of many computer vision and machine learning methods, including robust principal component analysis (RPCA) and low-rank representations (LRR). However, usual methods rely on optimization to produce a point estimate without characterizing uncertainty in this estimate, and also face difficulties in tuning parameter choice. Both of these limitations are potentially overcome with Bayesian methods, but there is currently a lack of general purpose Bayesian approaches for rank penalization. We address this gap using a positive generalized double Pareto prior, illustrating the approach in RPCA and LRR. Posterior computation relies on hybrid Gibbs sampling and geodesic Monte Carlo algorithms. We assess performance in simulation examples, and benchmark data sets.

Food or nutrient pattern assessment using the principal component analysis applied to food questionnaires. Pitfalls, tips and tricks.

We considered the Blom's transformation, a statistical method aimed to normalise and standardise food intakes before principal component analysis. A simulation study was performed to evaluate the eigenvalue distribution of a correlation matrix under common conditions in food questionnaire analysis. The scree plot visual inspection and the Guttman-Kaiser (GK) criterion were compared to Horn's parallel analysis to evaluate their efficacy in food pattern identification. The scree plot results as a monotone continuous series when no food patterns are present. In this situation, about 50% of the eigenvalues assume a value higher than one, showing a first fallacy of the GK. When three food patterns are simulated a clear discontinuity appears after the third eigenvalue, showing that the scree-plot visual inspection is a suitable method to identify food patterns. Finally, according to the present work it appears that the GK generates a number of false-positive food patterns.

Spatial distribution, source analysis, and health risk assessment of heavy metals contamination in house dust and surface soil from four major cities of Nepal.

Raising population, deteriorating environmental conditions and limiting natural resources to handle the key environmental health problems have critically affected human health and the environment. Policy makers and planners in Nepal are more concerned today than at any other time in the past about the deterioration of the environmental condition. Therefore, understanding the connection between pollution and human wellbeing is fundamental endeavors to control pollution exposures and secure human wellbeing. This ability is especially critical for countries like Nepal where the issues of environmental pollution have customarily taken a second place to request for economic development. In this study, spatial distribution and sources of 12 heavy metals (HMs) were investigated in surface soils (n = 24) and house dust (n = 24) from four major urban areas of Nepal in order to mark the pollution level. Additionally, a health risk was estimated to establish the link between HMs pollution and human health. Results showed that the median concentration of Ag, Cd, Co, Cr, Cu, Ni, Pb, Sb, Mn and Zn in soil and dust were 2-13 times greater than the background value. The As, Zn, Cu, Cd, and Pb showed a relatively higher spatial variability in soil and dust. Zn was the most abundant metal measured in dust and soil and accounted for 59% and 55% of ∑7HMs, respectively. The HMs in soil and dust were poorly correlated with total organic carbon (TOC) and black carbon (BC), suggesting little or no influence on HMs contamination. Source analysis study indicated the distribution of Cr, Ni, Sb, Ag, Pb, Cu, and Zn in soil and dust are mainly affected by anthropogenic sources, particularly traffic emissions, industrial source, and domestic households materials, while Co, Fe, As, Mn and Cd were from natural sources. The estimated carcinogenic risk (CR) of HMs in soil and dust exceeded the acceptable level of human exposure, recommending significant CR to the local population.

Transfer market activities and sportive performance in European first football leagues: A dynamic network approach.

Professional football is a globalized game in which players are the most valuable assets for clubs. In this study, we explore the evolution of the football players' transfer network among 21 European first leagues between the seasons 1996/1997 and 2015/2016. From a topological point of view, we show that this network achieved an upper limit expansion around season 2007/2008, thereafter becoming more connected and dense. Using a machine learning approach based on Self-Organizing Maps and Principal Component Analysis we confirm that European competitions, such as the UEFA Champions League or UEFA Europa League, are indeed a "money game" where the clubs with the highest transfer spending achieve better sportive performance. Some clubs' transfer market activities also affect domestic performance. We conclude from our findings that the relationship between transfer spending and domestic or international sportive performance might lead to substantial inequality between clubs and leagues, while potentially creating a virtuous (vicious) circle in which these variables reinforce (weaken) each other.

General Component Analysis (GCA): A new approach to identify Chinese corporate bond market structures.

PCA has been widely used in many fields to detect dominant principle components, but it ignores the information embedded in the remaining components. As a supplement to PCA, we propose the General Component Analysis (GCA). The inverse participation ratios (IPRs) are used to identify the global components (GCs) and localized components (LCs). The mean values of the IPRs derived from the shuffled data are taken as the natural threshold, which is exquisite and novel. In this paper, the Chinese corporate bond market is analyzed as an example. We propose a novel network method to divide time periods based on micro data, which performs better in capturing the time points when the market state switches. As a result, two periods have been obtained. There are two GCs in both periods, which are influenced by terms to maturity and ratings. Besides, there are 382 LCs in Period 1 and 166 LCs in Period 2. In the LC portfolios there are two interesting bond collections which are helpful to understand the thoughts of the investors. One is the supper AAA bond collection which is believed to have implicit governmental guarantees by the investors, and the other is the overcapacity industrial bond collection which is influenced by the supply-side reform led by the Chinese government. GCA is expected to be applied to other complex systems.

Quality Assessment of Kumu Injection, a Traditional Chinese Medicine Preparation, Using HPLC Combined with Chemometric Methods and Qualitative and Quantitative Analysis of Multiple Alkaloids by Single Marker.

Kumu injection (KMI) is a common-used traditional Chinese medicine (TCM) preparation made from Picrasma quassioides (D. Don) Benn. rich in alkaloids. An innovative technique for quality assessment of KMI was developed using high performance liquid chromatography (HPLC) combined with chemometric methods and qualitative and quantitative analysis of multi-components by single marker (QAMS). Nigakinone (PQ-6, 5-hydroxy-4-methoxycanthin-6-one), one of the most abundant alkaloids responsible for the major pharmacological activities of Kumu, was used as a reference substance. Six alkaloids in KMI were quantified, including 6-hydroxy-ß-carboline-1-carboxylic acid (PQ-1), 4,5-dimethoxycanthin-6-one (PQ-2), ß-carboline-1-carboxylic acid (PQ-3), ß-carboline-1-propanoic acid (PQ-4), 3-methylcanthin-5,6-dione (PQ-5), and PQ-6. Based on the outcomes of twenty batches of KMI samples, the contents of six alkaloids were used for further chemometric analysis. By hierarchical cluster analysis (HCA), radar plots, and principal component analysis (PCA), all the KMI samples could be categorized into three groups, which were closely related to production date and indicated the crucial influence of herbal raw material on end products of KMI. QAMS combined with chemometric analysis could accurately measure and clearly distinguish the different quality samples of KMI. Hence, QAMS is a feasible and promising method for the quality control of KMI.

Discriminant analysis of principal components and pedigree assessment of genetic diversity and population structure in a tetraploid potato panel using SNPs.

The reported narrow genetic base of cultivated potato (Solanum tuberosum) can be expanded by the introgression of many related species with large genetic diversity. The analysis of the genetic structure of a potato population is important to broaden the genetic base of breeding programs by the identification of different genetic pools. A panel composed by 231 diverse genotypes was characterized using single nucleotide polymorphism (SNP) markers of the Illumina Infinium Potato SNP Array V2 to identify population structure and assess genetic diversity using discriminant analysis of principal components (DAPC) and pedigree analysis. Results revealed the presence of five clusters within the populations differentiated principally by ploidy, taxonomy, origin and breeding program. The information obtained in this work could be readily used as a guide for parental introduction in new breeding programs that want to maximize variability by combination of contrasting variability sources such as those presented here.

Economic Inequality in Presenting Vision in Shahroud, Iran: Two Decomposition Methods.

BACKGROUND: Visual acuity, like many other health-related problems, does not have an equal distribution in terms of socio-economic factors. We conducted this study to estimate and decompose economic inequality in presenting visual acuity using two methods and to compare their results in a population aged 40-64 years in Shahroud, Iran. METHODS: The data of 5188 participants in the first phase of the Shahroud Cohort Eye Study, performed in 2009, were used for this study. Our outcome variable was presenting vision acuity (PVA) that was measured using LogMAR (logarithm of the minimum angle of resolution). The living standard variable used for estimation of inequality was the economic status and was constructed by principal component analysis on home assets. Inequality indices were concentration index and the gap between low and high economic groups. We decomposed these indices by the concentration index and BlinderOaxaca decomposition approaches respectively and compared the results. RESULTS: The concentration index of PVA was -0.245 (95% CI: -0.278, -0.212). The PVA gap between groups with a high and low economic status was 0.0705 and was in favor of the high economic group. Education, economic status, and age were the most important contributors of inequality in both concentration index and Blinder-Oaxaca decomposition. Percent contribution of these three factors in the concentration index and Blinder-Oaxaca decomposition was 41.1% vs. 43.4%, 25.4% vs. 19.1% and 15.2% vs. 16.2%, respectively. Other factors including gender, marital status, employment status and diabetes had minor contributions. CONCLUSION: This study showed that individuals with poorer visual acuity were more concentrated among people with a lower economic status. The main contributors of this inequality were similar in concentration index and Blinder-Oaxaca decomposition. So, it can be concluded that setting appropriate interventions to promote the literacy and income level in people with low economic status, formulating policies to address economic problems in the elderly, and paying more attention to their vision problems can help to alleviate economic inequality in visual acuity.

[Morbidity profile and hospital expenses with elderly patients in Paraná State, Brazil, 2008-2012]. / Perfil de morbidade e gastos hospitalares com idosos no Paraná, Brasil, entre 2008 e 2012.

Growth in the elderly population has increased both the demand for health services and healthcare expenses, with relevant consequences for economic stability. The current study aimed to analyze the morbidity profile and hospital expenses with elderly patients in relation to socioeconomic and demographic conditions in Paraná State, Brazil, from 2008 to 2012, applying principal components analysis and groupings. Regions with higher and lower economic and human development showed high prevalence of hospitalizations and costs pertaining to the circulatory and respiratory systems and cardiac diseases for males and females. In regions with intermediary development, diseases of the nervous system in men and of the circulatory system in women had the highest morbidity and hospital expenses. Thus, measures for prevention and health promotion in this elderly population should be targeted to regional investigations.

Relationship between regional population and healthcare delivery in Japan.

In order to address regional inequality in healthcare delivery in Japan, healthcare districts were established in 1985. However, regional healthcare delivery has now become a national issue because of population migration and the aging population. In this study, the state of healthcare delivery at the district level is examined by analyzing population, the number of physicians, and the number of hospital beds. The results indicate a continuing disparity in healthcare delivery among districts. We find that the rate of change in population has a strong positive correlation with that in the number of physicians and a weak positive correlation with that in the number of hospital beds. In addition, principal component analysis is performed on three variables: the rate of change in population, the number of physicians per capita, and the number of hospital beds per capita. This analysis suggests that the two principal components contribute 90.1% of the information. The first principal component is thought to show the effect of the regulations on hospital beds. The second principal component is thought to show the capacity to recruit physicians. This study indicates that an adjustment to the regulations on hospital beds as well as physician allocation by public funds may be key to resolving the impending issue of regionally disproportionate healthcare delivery.

Assessment of Functional Improvement without Compensation for Human Spinal Cord Injury: Extending the Neuromuscular Recovery Scale to the Upper Extremities.

The Neuromuscular Recovery Scale (NRS) is a tool for measuring functional recovery in spinal cord injured (SCI) persons based on tasks that test pre-injury functional capability. The NRS has been shown to be a valid, reliable, and responsive instrument for measuring functional recovery. The NRS has been updated to include three items measuring upper extremity function, and a new scoring mechanism has been defined. The purpose of this prospective, observational study was to explore the properties of the expanded NRS, introduce and evaluate the new scoring method, and to examine the score's relationship with other SCI outcome measures. The NRS and seven other SCI outcome measures were assessed at enrollment and after every 20 locomotor training sessions in 64 participants of the NeuroRecovery Network (NRN) of the Christopher and Dana Reeve Foundation (CDRF). The NRS exhibited a dominant first principal component that correlated strongly with the new NRS score, as well as a potential secondary component discriminating upper extremity function. The new NRS score and its empirical subscales were generally well-correlated with International Standards for the Neurological Classification of Spinal Cord Injury (ISNCSCI) motor scores and other established SCI functional measures, but exhibited substantial variability at their boundary values. The NRS score was more strongly correlated with other SCI functional measures than ISNCSCI motor scores were. The new NRS score was most responsive to change brought on by locomotor training. The expanded NRS appears to be a valuable tool in measuring functional recovery from SCI; further evaluation of its psychometric properties is warranted.

PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology.

BACKGROUND AND OBJECTIVE: Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. METHOD: This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). RESULTS: Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. CONCLUSIONS: This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions.