Diabetes Prediction: A Study of Various Classification based Data Mining Techniques

Data Mining is an integral part of KDD (Knowledge Discovery in Databases) process. It deals with discovering unknown patterns and knowledge hidden in data. Classification is a pivotal data mining technique with a very wide range of applications. Now a day’s diabetic has become a major disease which has almost crippled people across the globe. It is a medical condition that causes the metabolism to become dysfunctional and increases the blood sugar level in the body and it becomes a major concern for medical practitioner and people at large. An early diagnosis is the starting point for living well with diabetes. Classification Analysis on diabetic dataset is a part of this diagnosis process which can help to detect a diabetic patient from non-diabetic. In this paper classification algorithms are applied on the Pima Indian Diabetic Database which is collected from UCI Machine Learning Laboratory. Various classification algorithms which are Naïve Bayes Classifier, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Classifier and XGBoost Classifier are analyzed and compared based on the accuracy delivered by the models

Resolving blood group discrepancy in patients of tertiary care centre in Odisha, India

Background: Blood grouping consists of both forward grouping; reverse grouping and both procedures should agree with each other.A blood group discrepancy exists when results of red cell testing do not agree with serum testing, usually due to unexpected negative or positive results in either forward or reverse typing. ABO and Rh blood group discrepancy is associated with incompatible transfusion reaction.Blood group discrepancy should be resolved before transfusion and blood group to be properly labeled to prevent transfusion reaction.Methods: A prospective study was carried in SCB blood bank which is under the Department of Transfusion Medicine, SCB Medical College and Hospital, Cuttack, Odisha from January 2015 to October-2016. Total 25,559 blood samples of patients were included in the study and hemolysed samples excluded. The ABO and Rh D typing was done by tube technique using monoclonal IgM (Tulip Diagnostic P Ltd.) Anti-A, Anti-B, Anti-D and pooled A, B and O cell.Results: A total of 25,559 blood group testing were done where we found 57 blood group discrepancies with overall frequency was 0.22%. Out of 57 discrepancies we were found 20 (35.09%) cases of technical error and 37 (64.91%) cases of sample related error. Among these sample related problems, we found weak/missing antibody, weak antigen expression, rouleaux, cold autoantibodies, cold alloantibodies, Bombay phenotype with the frequency of 13.51%, 2.70%, 2.70%, 54.06%, 8.11%, 18.92% respectively.Conclusions: Mistyping either a donor or a recipient can lead to transfusion with ABO-incompatible blood, which can result in severe hemolysis and may even result in the death of the recipient. Any discrepancy between forward and reverse blood grouping methods should be resolved before transfusion of blood components

Energy Management Analysis of Residential Building Using ANN Techniques

  The process of limiting the amount of energy that is utilized is known as energy conservation. This can be accomplished by making more effective use of the energy that is available. As a result, there is a requirement for more effective management of the consumption of energy in buildings. It is essential to have an accurate load calculation for a residential building because the loads for heating and cooling add up a significant portion of the total building loads. In this study, the load analysis of the HVAC (Heating, Ventilation, and Air Conditioning) system in a residential building was carried out by taking into consideration three different neural networks. These networks are known as the feed forward network, the cascaded forward back propagation network, and the Elman back propagation network. During the process of conducting a load study of the heating and cooling loads on an HVAC system, performance measurements like MAE (mean absolute error), MSE (mean square error), MRE (mean relative error), and MAPE (mean absolute percentage error) are taken into consideration. It has been discovered that the cascaded forward back propagation method is the most effective method, with MAE, MSE, MRE, and MAPE values of 0.08, 0.0336, 0.0051, and 0.51% respectively for heating load and MAE, MSE, MRE, and MAPE values of 0.0975, 0.0406, 0.0053, and 0.53% respectively for cooling load

Platelet audit: To weigh the rationality between requirement and uses in blood transfusion

Background: Blood transfusion especially the transfusion of blood component is an important part for better patient management than whole blood transfusion. Despite various approved guidelines, non-compliance regarding rational use prevails in transfusion services.Methods: In the present study; retrospective audit was conducted for a period of six months in the department of Transfusion Medicine, SCB Medical College and Hospital revealed on 3871 number of platelets prepared.Results: Out of 3757 units of platelet issued, there was 10.9% group nonspecific platelet transfusion, 31% inappropriate platelet transfusion and 1.99% wastage.Conclusions: The goal of transfusion service is to provide adequate number of safe blood components to the patient requiring this transfusion as per clinical guideline. This can be achieved by platelet audit which plays an important tool to reduce the inappropriate transfusion in patients, by improving the practice, in adherence to guidelines and focusing the areas of pitfall

ESMP: A high-throughput computational pipeline for mining SSR markers from ESTs

With the advent of high-throughput sequencing technology, sequences from many genomes are being deposited to public databases at a brisk rate. Open access to large amount of expressed sequence tag (EST) data in the public databases has provided a powerful platform for simple sequence repeat (SSR) development in species where sequence information is not available. SSRs are markers of choice for their high reproducibility, abundant polymorphism and high inter-specific transferability. The mining of SSRs from ESTs requires different high-throughput computational tools that need to be executed individually which are computationally intensive and time consuming. To reduce the time lag and to streamline the cumbersome process of SSR mining from ESTs, we have developed a user-friendly, web-based EST-SSR pipeline “EST-SSR-MARKER PIPELINE (ESMP)”. This pipeline integrates EST pre-processing, clustering, assembly and subsequently mining of SSRs from assembled EST sequences. The mining of SSRs from ESTs provides valuable information on the abundance of SSRs in ESTs and will facilitate the development of markers for genetic analysis and related applications such as marker-assisted breeding

Mining for SSRs and FDMs from expressed sequence tags of Camellia sinensis

Simple Sequence Repeats (SSRs) developed from Expressed Sequence Tags (ESTs), known as EST-SSRs are most widely used and potentially valuable source of gene based markers for their high levels of crosstaxon portability, rapid and less expensive development. The EST sequence information in the publicly available databases is increasing in a faster rate. The emerging computational approach provides a better alternative process of development of SSR markers from the ESTs than the conventional methods. In the present study, 12,851 EST sequences of Camellia sinensis, downloaded from National Center for Biotechnology Information (NCBI) were mined for the development of Microsatellites. 6148 (4779 singletons and 1369 contigs) non redundant EST sequences were found after preprocessing and assembly of these sequences using various computational tools. Out of total 3822.68 kb sequence examined, 1636 (26.61%) EST sequences containing 2371 SSRs were detected with a density of 1 SSR/1.61 kb leading to development of 245 primer pairs. These mined EST-SSR markers will help further in the study of variability, mapping, evolutionary relationship in Camellia sinensis. In addition, these developed SSRs can also be applied for various studies across species

Evaluation and comparison of the constitutive expression levels of Toll-like receptors 2, 3 and 7 in the peripheral blood mononuclear cells of Tharparkar and crossbred cattle

Aim: This study was undertaken to assess the differential expression levels of toll-like receptors (TLRs) 2, 3 and 7 in peripheral blood mononuclear cells (PBMCs) isolated from Tharparkar and Crossbred cattle belonging to different regions of India. Materials and Methods: PBMCs were isolated from blood samples of Tharparkar cattle from Indian Veterinary Research Institute (IVRI) farm (n=30); Suratgarh farm (n=61); Jaipur farm (n=8) and cross breed cattle from Jaipur (n=47). RNA was isolated from PBMCs and cDNA was synthesized using random hexamers. The expression profiles of TLR 2, 3 and 7 were estimated by real-time PCR and normalized to the expression of β-actin. Results: PBMCs of Tharparkar cattle from Suratgarh, exhibited a significantly higher (p<0.05) constitutive expression levels of TLR2, TLR3 and TLR7 genes as compared to Tharparkar cattle from IVRI or Jaipur as well as the crossbred cattle from Jaipur. PBMCs of crossbred cattle from Jaipur showed higher expression profiles of all the TLRs than Tharparkar cattle from Jaipur and IVRI. Conclusion: Our study indicates, expression levels of TLR2, TLR3 and TLR7 are significantly higher for Tharparkar cattle from Suratgarh than the cattle from Jaipur and IVRI and crossbred cattle from Jaipur. However, crossbred cattle from Jaipur showed higher basal expression levels of all the three TLRs than Tharparkar cattle from Jaipur and IVRI. Results also indicate that PBMCs of Tharparkar cattle show a regional variation in the expression pattern of TLRs

Mechanical, Thermal, and Interfacial Characterization of Randomly Oriented Short Sisal Fibers Reinforced Epoxy Composite Modified with Epoxidized Soybean Oil

Short sisal fibers were reinforced in epoxidized soybean oil (ESO) modified toughened epoxy blends to improve the mechanical and thermo mechanical properties. Tensile modulus and tensile strength of the composite with 15 wt% sisal fiber were found to be increased as compared with bio-based epoxy blend. From DTG analysis, rate of degradation peak is found to be shifted to higher temperature revealing enhanced thermal stability of composite over base matrix. Dynamic mechanical analysis predicted higher storage modulus and higher glass transition temperature of bio-based epoxy composite. Scanning electron micrographs showed strong fiber-matrix adhesion. Contact angle measurement reveals the hydrophilic character of bio-based epoxy composit