Pingale et al., 2019 - Google Patents
Disease prediction using machine learningPingale et al., 2019
View PDF- Document ID
- 2489201816131722454
- Author
- Pingale K
- Surwase S
- Kulkarni V
- Sarage S
- Karve A
- Publication year
- Publication venue
- International Research Journal of Engineering and Technology (IRJET)
External Links
Snippet
Disease Prediction system is based on predictive modeling predicts the disease of the user on the basis of the symptoms that user provides as an input to the system. The system analyzes the symptoms provided by the user as input and gives the probability of the …
- 201000010099 disease 0 title abstract description 39
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-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
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Pingale et al. | Disease prediction using machine learning | |
Qin et al. | A machine learning methodology for diagnosing chronic kidney disease | |
Naraei et al. | Application of multilayer perceptron neural networks and support vector machines in classification of healthcare data | |
Shamrat et al. | Implementation of machine learning algorithms to detect the prognosis rate of kidney disease | |
Chitra et al. | Heart disease prediction system using supervised learning classifier | |
Gomathy et al. | The prediction of disease using machine learning | |
Tafa et al. | An intelligent system for diabetes prediction | |
Theerthagiri et al. | Prediction of COVID-19 possibilities using KNN classification algorithm | |
Begum et al. | Classification of physiological signals for wheel loader operators using multi-scale entropy analysis and case-based reasoning | |
Jiang et al. | A resilient and hierarchical IoT-based solution for stress monitoring in everyday settings | |
Folorunso et al. | Heart disease classification using machine learning models | |
Lohita et al. | Performance analysis of various data mining techniques in the prediction of heart disease | |
Salih et al. | Benchmarking framework for COVID-19 classification machine learning method based on fuzzy decision by opinion score method | |
Sherly | An ensemble basedheart disease predictionusing gradient boosting decision tree | |
Alshammari | Applying Machine Learning Algorithms for the Classification of Sleep Disorders | |
Chowdhary et al. | Non-invasive Detection of Parkinson's Disease Using Deep Learning | |
Karthikeyini et al. | Heart disease prognosis using D-GRU with logistic chaos honey badger optimization in IoMT framework | |
Nnamoko et al. | Meta-classification model for diabetes onset forecast: A proof of concept | |
Deepa et al. | Action fuzzy rule based classifier for analysis of dermatology databases | |
Kulkarni et al. | ‘Prediction of disease using machine learning | |
Kumar et al. | Diabetes prediction using machine learning tools | |
Shirwaikar et al. | Supervised learning techniques for analysis of neonatal data | |
Akbar et al. | An Intangible System to Augment the Prediction of Heart Diseases Using Machine Learning Techniques | |
Al-Bwana | Coronavirus (COVID-19) Detection using Ensemble Learning | |
Dhilsath Fathima et al. | Hddss: An enhanced heart disease decision support system using rfe-abgnb algorithm |