Nothing Special   »   [go: up one dir, main page]

Lohita et al., 2015 - Google Patents

Performance analysis of various data mining techniques in the prediction of heart disease

Lohita et al., 2015

View PDF
Document ID
14002068259896739201
Author
Lohita K
Sree A
Poojitha D
Devi T
Umamakeswari A
Publication year
Publication venue
Indian Journal of Science and Technology

External Links

Snippet

Objective: The main objective of the work is to compare the heart disease prediction accuracy of different data mining classification technique and to find the best technique with minimum incorrectly classified instances. Different classification techniques are used to …
Continue reading at sciresol.s3.us-east-2.amazonaws.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587Details of specialised database models
    • G06F17/30595Relational databases
    • G06F17/30598Clustering or classification
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705Clustering or classification
    • G06F17/3071Clustering or classification including class or cluster creation or modification
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/24Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • G06F15/18Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6279Classification techniques relating to the number of classes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation

Similar Documents

Publication Publication Date Title
Islam et al. Chronic kidney disease prediction based on machine learning algorithms
Akella et al. Machine learning algorithms for predicting coronary artery disease: efforts toward an open source solution
Sakr et al. Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project
Jan et al. Ensemble approach for developing a smart heart disease prediction system using classification algorithms
Jongbo et al. Development of an ensemble approach to chronic kidney disease diagnosis
Peter et al. An empirical study on prediction of heart disease using classification data mining techniques
Lohita et al. Performance analysis of various data mining techniques in the prediction of heart disease
Almazroi et al. A clinical decision support system for heart disease prediction using deep learning
Sami et al. The role of data pre-processing techniques in improving machine learning accuracy for predicting coronary heart disease
Liu et al. Predictive analytics for blood glucose concentration: an empirical study using the tree-based ensemble approach
Rekabdar et al. From machine learning to deep learning: A comprehensive study of alcohol and drug use disorder
Lin et al. Acute coronary syndrome risk prediction based on gradient boosted tree feature selection and recursive feature elimination: A dataset-specific modeling study
Saif et al. Deep-kidney: an effective deep learning framework for chronic kidney disease prediction
Obayya et al. Automated cardiovascular disease diagnosis using Honey Badger Optimization with modified deep learning model
Mukherjee et al. Intelligent heart disease prediction using neural network
Zhou et al. Machine learning methods in real-world studies of cardiovascular disease
Chippalakatti et al. Comparative review on the machine learning algorithms for medical data
Rahman et al. Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response
Rao et al. Medical Big Data Analysis using LSTM based Co-Learning Model with Whale Optimization Approach.
Navaz et al. The use of data mining techniques to predict mortality and length of stay in an ICU
Elsayyad et al. Cardiac arrhythmia classification using boosted decision trees
Zou et al. A narrative review of the application of machine learning in venous thromboembolism
Hassan et al. CNN-CardioAssistant: Deep Convolutional Neural Network and Recursive Feature Elimination Method for Heart Disease Detection
Ghias et al. Using Machine Learning Algorithms to predict sepsis and its stages in ICU patients
Riyaz et al. Ensemble learning for coronary heart disease prediction