Application of Bayesian analysis on risk factors of coronary artery disease

Ischemic heart disease (or Coronary Artery Disease) is the most common cause of death in various countries, characterized by reduced blood supply to the heart. Statistical models make an impact for evaluating the risk factors which are responsible for mortality and morbidity during IHD (Ischemic heart disease). In this work, due to count data, we propose Poisson, Negative Binomial and also utilize a flexible class of zero inflated models such as Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) models estimated by the method of MLE and are compared to assess the most appropriate model for the underlying data.  The forward and backward model selection procedures are also taken to permit the most significant factors associated with heart disease. The ZIP model is identified as the most appropriate one in this work. Moreover, a Bayesian estimation is chosen to account for prior on regression coefficients in a small sample size setting. This estimation also evolves as an alternative to traditionally used MLE based methods for such data. As per our simulation studies: the proposed method has better finite sample performance than the classical method with tighter interval estimates and better coverage probabilities. The simulation is based on R-software

Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart Disease

Ischemic heart disease (or Coronary Artery Disease) is the most common cause of death in various countries, characterized by reduced blood supply to the heart. Statistical models make an impact in evaluating the risk factors that are responsible for mortality and morbidity during IHD (Ischemic heart disease). In general, geometric or Poisson distributions can underestimate the zero-count probability and hence make it difficult to identify significant effects of covariates for improving conditions of heart disease due to regional wall motion abnormalities. In this work, a flexible class of zero inflated models is introduced. A Bayesian estimation method is developed as an alternative to traditionally used maximum likelihood-based methods to analyze such data. Simulation studies show that the proposed method has a better small sample performance than the classical method, with tighter interval estimates and better coverage probabilities. Although the prevention of CAD has long been a focus of public health policy, clinical medicine, and biomedical scientific investigation, the prevalence of CAD remains high despite current strategies for prevention and treatment. Various comprehensive searches have been performed in the MEDLINE, HealthSTAR, and Global Health databases for providing insights into the effects of traditional and emerging risk factors of CAD. A real-life data set is illustrated for the proposed method using WinBUGS.This research was funded by the Spanish Government for its support through grant RTI2018-094336-B-100 (MCIU/AEI/FEDER, UE) and to the Basque Government for its support through grant IT1207-19

Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy

Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ROC curve for mini-MIAS are 91.39% and 0.964 respectively which give the confirmation of the effectiveness of the proposed algorithm (FCM-based SVM). This method increases the classification accuracy in the case of a malignant tumor. The simulation is based on R-software.This research was funded by the Spanish Government for its support through grant RTI2018-094336-B-100 (MCIU/AEI/FEDER, UE) and to the Basque Government for its support through grant IT1207-19

Predicting factors and top gene identification for survival data of breast cancer

For high-throughput research with biological data-sets generated sequentially or by transcriptional micro-arrays, proteomics or other means, analytic techniques that address their high dimensional aspects remain desirable. The computation part basically predicts the tendency towards mortality due to breast cancer (BC) by using several classification methods, i.e., Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Decision Tree (DT), and compared the models' performances. We proceed with the RF method since it provides better results than any other underlying models based on accuracy. We have also demonstrated some traditional and competing risk models, illustrated the models with real data analysis, depicted their curves' natures and also compared their fits using prediction error curves and the concordance index. Furthermore, two different survival splitting rules are used by using separate Random Survival Forest (RSF) methods and also constructing the ranking of risk factors due to breast cancer. The results show that high-level grade and diameter are the most important predictors for mortality progression in the presence of competing events of death, and lymph nodes, age and angiography are other vital criteria for this purpose. We have also implemented RSF backward selection criteria, which enables top gene selection related to mortality progression due to breast cancer. This method identifies c-MYB, CDCA7, NUSAP1, BIRC5, ANGPTL4, JAG1, IL6ST, and remaining genes that are mainly responsible for mortality progression due to breast cancer. In this work, R software is used to obtain and evaluate the results

Appraisal of trawl fisheries of India with special reference on the changing trends in bycatch utilization

Trawl fisheries sector account more than 50% of the marine fisheries production of India. Annual average fish landing from trawlers was 17, 21, 000 t (2008-2011), which formed around 51 % of the marine fish landing of the coast. In this about 51% of the catch was contributed by the west coast and remaining by the east coast of India. Recent studies of the trawl fishery in India have shown that incidental catches/low value bycatch (LVB) landing and utilization has increased over the period of time. The present study is based on the data collected from major trawl landing centres along the coast of India during the period 2008-2011.The estimated landing of low value bycatch (LVB) in trawl fisheries, increased from 14 % in 2008 to 25 % in 2011, which is reflected as reduction in discard volume by trawlers. On an average the highest quantity of LVB landed was in Veraval (50,000 t) and in Mangalore, LVB landing increased from 3% in (3000 t) in 2008 to 26 % (12,000 t) in 2011.In Mumbai, the percentage of trash fish landed remained around 5% during the study period. In Calicut, the LVB landed in 2011 contributed 26% to the total landings by the trawl. In Kochi, Kerala the total LVB landed in 2011 was 1,992 t forming 7.2 % of the total landing. In Chennai, Tamilnadu, the LVB landing which was 13 % in 2008 increased to 17% in 2011, while in Visakhapatanam, Andhra Pradesh, LVB landing showed a steady increase from 2% in 2008 to 21% in 2011. The landing centre price for LVB showed an increasing trend due to increased demand for trash fish for the production of fish meal and fertilizer. The dominance of finfishes in LVB found to increase the value of LVB and the value realized for 30,000 t of LVB in Available online at: www.mbai.org.in doi: 10.6024/jmbai.2013.55.2.01765-11 Mangalore in 2011 ( Rs.280 million) was more than that realized for 50,000 t of LVB in Veraval (Rs.200 million). A disturbing trend observed from the studies in Mangalore was that, the sardines in trash fetches higher price in some seasons (upto Rs.16/kg) compared to a lower price when landed in fresh form, and the percentage of sardines in LVB is found to be very high (24% in 2010). This trend may cause a severe threat to the protein availability to the rural poor. Looking at the trend of trawl landing during 2008-2012, it is seen that even though the trawl landing showed an increase over the period of time, the edible portion of the trawl landing did not show any significant increase rather showing general declining trend. Study on the bycatch from different centres along the coast of India showed that as many as 237 species / groups of marine fauna with juveniles of commercially important fishes were landed as LVB. Increase in utilization of LVB (which was discarded earlier) from trawl fishery, a trend which is market driven and its implication on the ecosystem and marine fisheries production of the country is discussed in this paper

Bycatch in Indian trawl fisheries and some suggestions for trawl bycatch mitigation

Globally, trawl is the major fishing gear used in marine fisheries and in India, it contributes to more than onethird of the marine fish production. Trawl fishing has been critically evaluated from a sustainability perspective, especially analysing its bycatch composition. Most of the bycatch from trawlers contains valuable edible species with high market demand. However, a portion of the bycatch which does not have such demand in the edible fish market, known as low-value bycatch (LVB), continues to be a matter of concern from an ecological and economic perspective. During 2017–19, 30–60% of trawl landing in India was constituted by LVB, which was mainly used for fishmeal preparation. To enhance the value and utility of LVB, this study explores the possibility of converting waste from LVB into edible resources using pufferfish and triggerfish. It also highlights the positive impact of efforts by different Government agencies for bycatch mitigation like the implementation of minimum legal size in reducing the juvenile component in bycatch, with a social survey-based account of fisher’s perceptions and suggestions on successful bycatch mitigation

Towards quantifying the relative tectonic activity in the Trans-Yamuna segment of NW Himalaya

Owing to the increased availability of high-resolution satellite data and the rapid development of Geographic Information System (GIS) technology, the mapping of active faults and quantification of tectonic activity in inaccessible regions has exceedingly improved. We examined the tectonic activity in the Trans-Yamuna region of the NW Himalaya using geomorphic indices derived from a Digital Elevation Model (DEM). In addition, this study evaluates the sensitivity of four space-borne Digital Elevation Models (DEMs) with respect to TAN DEM-X (TerraSAR-X add-on for Digital Elevation Measurements). The Cartosat DEM, generated with a spatial resolution of 5 meters using state-of-the-art methods, demonstrated a reliable representation of topography. Geomorphic indices such as Asymmetry Factor (AF), Transverse Topography (TT), Hypsometric Integral (HI), Valley Floor width (Vf), Stream-length gradient index (SL), and Normalised steepness index (ksn) were computed for 41 sub-watersheds to determine the degree of tectonic activity. We infer that majority of the region is tectonically active, with upliftment continuing to occur in the north of the Main Boundary Thrust (MBT). Furthermore, the existence of mapped active faults north of the MBT further substantiates the fact that strain release is not only concentrated in the frontal Himalaya, but is distributed over a broader area above the decollement