Bioinformatics || Risk of Cardiovascular disease analysis in Bioinformatics framework
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This document summarizes a student project analyzing cardiovascular disease risk using bioinformatics. It introduces bioinformatics and describes how gene expression analysis and genetic algorithms can be used to study cardiovascular diseases like atherosclerosis and predict disease risk. The project uses a genetic algorithm approach involving feature selection, initial population creation, selection, crossover, and mutation to analyze a cardiovascular disease dataset and identify the individual with the best fitness.
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MEDLINE
MEDLINE is a literature database of biomedical information introduced in 1971 and maintained by the United States National Library of Medicine. It includes over 6,000 biomedical journals that are indexed and searchable via PubMed. MEDLINE began as MEDLARS, a computerized storage and retrieval system operated by the NLM since 1964 to index and provide access to medical literature. It has evolved to include additional databases and online services like PubMed and MedlinePlus.
Dept. Biomedical Engineering-A&M
The Biomedical Engineering department at Texas A&M University has experienced significant growth, with over 500 students and $5 million in research funding. It has 17 tenure-track professors and conducts research in areas such as vascular biomechanics, biomaterials, and tissue microscopy. The department works on medical applications including cancer detection, glucose monitoring, and treating cerebral aneurysms. It has strong industry partnerships and several faculty start-ups, and collaborates with institutions worldwide.
Genome feature optimization and coronary artery disease prediction using cuck...
Cardiovascular diseases are among the major health ailment issue leading to millions of deaths every year. In recent past, analyzing gene expression data, particularly using machine learning strategies to predict and classify the given unlabeled gene expression record is a generous research issue. Concerning this, a substantial requirement is feature optimization, which is since the overall genes observed in human body are closely 25000 and among them 636 are cardiovascular related genes. Hence, it complexes the process of training the machine learning models using these entire cardiovascular gene features. This manuscript uses bidirectional pooled variance strategy of ANOVA standard to select optimal features. Along the side to surpass the constraint observed in traditional classifiers, which is unstable accuracy at k-fold cross validation, this manuscript proposed a classification strategy that build upon the swarm intelligence technique called cuckoo search. The experimental study indicating that the number of optimal features those selected by proposed model is substantially low that compared to the other contemporary model that selects features using forward feature selection and classifies using support vector machine classifier (FFS&SVM). The experimental study evinced that the proposed model, which selects feature by bidirectional pooled variance estimation and classifies using proposed classification strategy that build on cuckoo search (BPVE&CS) outperformed the selected contemporary model (FFS&SVM).
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SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
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This document presents a new method for diagnosing coronary artery disease (CAD) using genetic algorithm (GA) wrapped Bayes naive (BN) feature selection. The method uses a GA to generate feature subsets that are evaluated using BN classification. Over multiple iterations, the GA selects the feature subset that provides the highest accuracy. The algorithm is tested on a CAD dataset containing 13 features and achieves 85.5% classification accuracy. This performance is compared to other machine learning algorithms like SVM, MLP and C4.5 decision trees, which achieve lower accuracies of 83.5%, 83.16% and 80.85% respectively. The proposed method is also compared to other feature selection techniques like best first search and sequential floating forward search wrapped
Is661 medical terms definitions
The document defines several key terms related to medical informatics and computational biology. It provides definitions of medical informatics from various experts that describe it as the intersection of information science, computer science, social science, behavioral science and healthcare. It deals with optimizing the acquisition, storage, retrieval, and use of medical information. Bioinformatics is defined as using computers to collect, classify, store, and analyze biochemical and biological information, especially related to molecular genetics and genomics. Computational biology develops methods and software tools for understanding biological data by combining computer science, statistics, mathematics and engineering to study biological data.
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MEDLINE
MEDLINE is a literature database of biomedical information introduced in 1971 and maintained by the United States National Library of Medicine. It includes over 6,000 biomedical journals that are indexed and searchable via PubMed. MEDLINE began as MEDLARS, a computerized storage and retrieval system operated by the NLM since 1964 to index and provide access to medical literature. It has evolved to include additional databases and online services like PubMed and MedlinePlus.
Dept. Biomedical Engineering-A&M
The Biomedical Engineering department at Texas A&M University has experienced significant growth, with over 500 students and $5 million in research funding. It has 17 tenure-track professors and conducts research in areas such as vascular biomechanics, biomaterials, and tissue microscopy. The department works on medical applications including cancer detection, glucose monitoring, and treating cerebral aneurysms. It has strong industry partnerships and several faculty start-ups, and collaborates with institutions worldwide.
Genome feature optimization and coronary artery disease prediction using cuck...
Cardiovascular diseases are among the major health ailment issue leading to millions of deaths every year. In recent past, analyzing gene expression data, particularly using machine learning strategies to predict and classify the given unlabeled gene expression record is a generous research issue. Concerning this, a substantial requirement is feature optimization, which is since the overall genes observed in human body are closely 25000 and among them 636 are cardiovascular related genes. Hence, it complexes the process of training the machine learning models using these entire cardiovascular gene features. This manuscript uses bidirectional pooled variance strategy of ANOVA standard to select optimal features. Along the side to surpass the constraint observed in traditional classifiers, which is unstable accuracy at k-fold cross validation, this manuscript proposed a classification strategy that build upon the swarm intelligence technique called cuckoo search. The experimental study indicating that the number of optimal features those selected by proposed model is substantially low that compared to the other contemporary model that selects features using forward feature selection and classifies using support vector machine classifier (FFS&SVM). The experimental study evinced that the proposed model, which selects feature by bidirectional pooled variance estimation and classifies using proposed classification strategy that build on cuckoo search (BPVE&CS) outperformed the selected contemporary model (FFS&SVM).
Bioinformatics
Introduction, Objective of Bioinformatics,
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IRJET - Effective Heart Disease Prediction using Distinct Machine Learning Te...
This document discusses using machine learning techniques to predict heart disease. It proposes a new hybrid method combining machine learning techniques to improve prediction accuracy. Specifically, it introduces a prediction model using different feature combinations and classification techniques, including a hybrid random forest with linear model (HRFLM) that achieves 88.7% accuracy. Previous studies predicting heart disease using machine learning are also discussed, but the authors aim to develop a unique method focusing on feature selection to further improve prediction accuracy of heart disease.
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
Supervised Feature Selection for Diagnosis of Coronary Artery Disease Based o...
This document presents a new method for diagnosing coronary artery disease (CAD) using genetic algorithm (GA) wrapped Bayes naive (BN) feature selection. The method uses a GA to generate feature subsets that are evaluated using BN classification. Over multiple iterations, the GA selects the feature subset that provides the highest accuracy. The algorithm is tested on a CAD dataset containing 13 features and achieves 85.5% classification accuracy. This performance is compared to other machine learning algorithms like SVM, MLP and C4.5 decision trees, which achieve lower accuracies of 83.5%, 83.16% and 80.85% respectively. The proposed method is also compared to other feature selection techniques like best first search and sequential floating forward search wrapped
Is661 medical terms definitions
The document defines several key terms related to medical informatics and computational biology. It provides definitions of medical informatics from various experts that describe it as the intersection of information science, computer science, social science, behavioral science and healthcare. It deals with optimizing the acquisition, storage, retrieval, and use of medical information. Bioinformatics is defined as using computers to collect, classify, store, and analyze biochemical and biological information, especially related to molecular genetics and genomics. Computational biology develops methods and software tools for understanding biological data by combining computer science, statistics, mathematics and engineering to study biological data.
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Sharon McDowell Hall
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Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
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Chapter 5
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Chapter 6
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2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
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- 1. Risk of Cardiovascular disease analysis in Bioinformatics framework A project by: Agnish, Anindya, Gopal, Rashbihari IT-4th year
- 2. Introduction • Bioinformatics is the use of computers for the acquisition, management, and analysis of biological information. • Bioinformatics is emerging and advance branch of biological science , contain Biology mathematics and Computer Science. • Bioinformatics is clearly a multi-disciplinary field including: computer systems management networking, database design, computer programming, molecular -- biology.
- 3. Gene Expression Analysis of Cardiovascular Diseases Cardiovascular diseases:- a. Atherosclerosis b. Atrial Fibrillation c. Heart Failure d. Hypertension…. etc. Treatment through Gene Therapy:- a. Identifying candidate Gene through RNA profiling. b. Cancer genomics as a template for cardiovascular medicine:- The Cancer literature provides us with some guidance how gene expression data might be applied in context of cardiovascular medicine.
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- 5. Flow Chart for GA Heart disease(Cardiovascul ar) data set Create fitness function for GA i.e f(x) Create an initial population Selection (Parents1,Parents 2) Crossover Mutation Stop Best individual (Child c1) Calculate distance between the parents and child chromosome and consider the f(x) Result Load Data(Selected Feature) Start StopNo Yes
- 6. References Our respected mentor Annwesha madam helping us a lot for doing this project. We are also getting help from the following links… 1. www.ijltet.org 2. www.iraj.com 3. https://en.wikipedia.org/wiki/Cardiovascular_disease 4. https://orwh.od.nih.gov/sites/orwh/files/docs/ORWH-HIC-Cardiovascular- Disease.pdf 5. www.who.int/cardiovascular_diseases/guidelines/Full%20text.pdf