Class information for: |
Basic class information |
| Class id | #P | Avg. number of references |
Database coverage of references |
|---|---|---|---|
| 35331 | 99 | 26.4 | 44% |
Hierarchy of classes |
The table includes all classes above and classes immediately below the current class. |
| Cluster id | Level | Cluster label | #P |
|---|---|---|---|
| 9 | 4 | COMPUTER SCIENCE, THEORY & METHODS//COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE//COMPUTER SCIENCE, INFORMATION SYSTEMS | 1247339 |
| 211 | 3 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE//IEEE TRANSACTIONS ON NEURAL NETWORKS//DATA MINING | 51632 |
| 3439 | 2 | SYSTEMAL METHOD//NEURAL NETWORKS COMPUTER//THEORY MED | 1542 |
| 35331 | 1 | ATTITUDES AND MANAGEMENT//CLINICAL TEST DATA//DISTILLATION ANALYSIS | 99 |
Terms with highest relevance score |
| rank | Term | termType | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
|---|---|---|---|---|---|---|
| 1 | ATTITUDES AND MANAGEMENT | authKW | 308441 | 1% | 100% | 1 |
| 2 | CLINICAL TEST DATA | authKW | 308441 | 1% | 100% | 1 |
| 3 | DISTILLATION ANALYSIS | authKW | 308441 | 1% | 100% | 1 |
| 4 | END POINT SURVEILLANCE | authKW | 308441 | 1% | 100% | 1 |
| 5 | FACTOR MEASUREMENTS | authKW | 308441 | 1% | 100% | 1 |
| 6 | FUELS ANALYSES | address | 308441 | 1% | 100% | 1 |
| 7 | KNOWLEDGE DISCOVERY PROCESS KDP | authKW | 308441 | 1% | 100% | 1 |
| 8 | MAGNETIC CONTENTS | authKW | 308441 | 1% | 100% | 1 |
| 9 | MEDICINE RELATED COMMON SENSE | authKW | 308441 | 1% | 100% | 1 |
| 10 | OCCUPATIONAL HEALTH CARE PHYSICIANS | authKW | 308441 | 1% | 100% | 1 |
Web of Science journal categories |
| Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
|---|---|---|---|---|---|
| 1 | Medical Informatics | 1686 | 16% | 0% | 16 |
| 2 | Medicine, General & Internal | 356 | 28% | 0% | 28 |
| 3 | Health Care Sciences & Services | 250 | 11% | 0% | 11 |
| 4 | Primary Health Care | 246 | 5% | 0% | 5 |
| 5 | Engineering, Biomedical | 216 | 12% | 0% | 12 |
| 6 | Computer Science, Artificial Intelligence | 164 | 11% | 0% | 11 |
| 7 | Operations Research & Management Science | 86 | 7% | 0% | 7 |
| 8 | Computer Science, Interdisciplinary Applications | 57 | 7% | 0% | 7 |
| 9 | Computer Science, Theory & Methods | 55 | 7% | 0% | 7 |
| 10 | Health Policy & Services | 53 | 4% | 0% | 4 |
Address terms |
| Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
|---|---|---|---|---|---|
| 1 | FUELS ANALYSES | 308441 | 1% | 100% | 1 |
| 2 | ARTS BUSINESS INFORMAT EDUC | 72571 | 2% | 12% | 2 |
| 3 | PULM PALLIAT CARE UNIT | 38553 | 1% | 13% | 1 |
| 4 | KALMAR CTY COUNCIL | 34269 | 1% | 11% | 1 |
| 5 | POST PROGRAM FOOD TECHNOL | 23724 | 1% | 8% | 1 |
| 6 | POST PROGRAM FOOD TECHNOL PPGTA | 14686 | 1% | 5% | 1 |
| 7 | CNPSO | 14018 | 1% | 5% | 1 |
| 8 | NEUROAD TAT GRP | 11422 | 1% | 4% | 1 |
| 9 | COORDINATING HLTH PROMOT | 6293 | 1% | 2% | 1 |
| 10 | PROGRAMA POS TECNOL ALIMENTOS | 5506 | 1% | 2% | 1 |
Journals |
| Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
|---|---|---|---|---|---|
| 1 | CARDIOVASCULAR RISK FACTORS | 8115 | 1% | 3% | 1 |
| 2 | ACTA MEDICA SCANDINAVICA | 5344 | 6% | 0% | 6 |
| 3 | IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE | 3768 | 4% | 0% | 4 |
| 4 | SCANDINAVIAN JOURNAL OF PRIMARY HEALTH CARE | 3002 | 3% | 0% | 3 |
| 5 | ACTA SCIENTIARUM-TECHNOLOGY | 2176 | 2% | 0% | 2 |
| 6 | KUWAIT JOURNAL OF SCIENCE | 1988 | 1% | 1% | 1 |
| 7 | INFORMATICS FOR HEALTH & SOCIAL CARE | 1556 | 1% | 1% | 1 |
| 8 | INTERNATIONAL JOURNAL OF BIO-MEDICAL COMPUTING | 1519 | 2% | 0% | 2 |
| 9 | MEDICAL INFORMATICS AND THE INTERNET IN MEDICINE | 1460 | 1% | 0% | 1 |
| 10 | IMA JOURNAL OF MATHEMATICS APPLIED IN MEDICINE AND BIOLOGY | 1088 | 1% | 0% | 1 |
Author Key Words |
Core articles |
The table includes core articles in the class. The following variables is taken into account for the relevance score of an article in a cluster c: (1) Number of references referring to publications in the class. (2) Share of total number of active references referring to publications in the class. (3) Age of the article. New articles get higher score than old articles. (4) Citation rate, normalized to year. |
| Rank | Reference | # ref. in cl. |
Shr. of ref. in cl. |
Citations |
|---|---|---|---|---|
| 1 | IFTIKHAR, F , SHAMS, A , DILAWARI, A , (2013) RCD: A TOOLKIT FOR RHEUMATIC VALVULAR AND CONGENITAL HEART DEFECT DIAGNOSIS.NEURAL COMPUTING & APPLICATIONS. VOL. 23. ISSUE 6. P. 1729-1735 | 2 | 100% | 1 |
| 2 | SILVA, LRC , ANGILELLI, KG , CREMASCO, H , ROMAGNOLI, ES , GALAO, OF , BORSATO, D , MORAES, LAC , MANDARINO, JMG , (2016) APPLICATION OF SELF-ORGANISING MAPS TOWARDS SEGMENTATION OF SOYBEAN SAMPLES BY DETERMINATION OF AMINO ACIDS CONCENTRATION.PLANT PHYSIOLOGY AND BIOCHEMISTRY. VOL. 106. ISSUE . P. 264 -268 | 5 | 29% | 0 |
| 3 | LINDBLAD, U , RASTAM, L , RANSTAM, J , PETERSON, M , (1993) VALIDITY OF REGISTER DATA ON ACUTE MYOCARDIAL-INFARCTION AND ACUTE STROKE - THE SKARABORG HYPERTENSION PROJECT.SCANDINAVIAN JOURNAL OF SOCIAL MEDICINE. VOL. 21. ISSUE 1. P. 3-9 | 4 | 57% | 108 |
| 4 | ROMAGNOLI, ES , SILVA, LRC , ANGILELLI, KG , FERREIRA, BAD , WALKOFF, AR , BORSATO, D , (2016) THE USE OF MULTILAYER PERCEPTRON ARTIFICIAL NEURAL NETWORKS FOR THE CLASSIFICATION OF ETHANOL SAMPLES BY COMMERCIALIZATION REGION.ACTA SCIENTIARUM-TECHNOLOGY. VOL. 38. ISSUE 2. P. 227 -232 | 4 | 33% | 0 |
| 5 | SRIDHAR, S , (2013) IMPROVING DIAGNOSTIC ACCURACY USING AGENT-BASED DISTRIBUTED DATA MINING SYSTEM.INFORMATICS FOR HEALTH & SOCIAL CARE. VOL. 38. ISSUE 3. P. 182 -195 | 2 | 67% | 1 |
| 6 | RASTAM, L , BERGLUND, G , ISACSSON, SO , RYDEN, L , (1986) THE SKARABORG HYPERTENSION PROJECT .3. INFLUENCE ON BLOOD-PRESSURE OF A MEDICAL-CARE PROGRAM FOR HYPERTENSION.ACTA MEDICA SCANDINAVICA. VOL. 219. ISSUE 3. P. 261 -269 | 3 | 100% | 5 |
| 7 | RASTAM, L , BERGLUND, G , ISACSSON, SO , RYDEN, L , (1986) THE SKARABORG HYPERTENSION PROJECT .2. FEASIBILITY OF A MEDICAL-CARE PROGRAM FOR HYPERTENSION.ACTA MEDICA SCANDINAVICA. VOL. 219. ISSUE 3. P. 249 -260 | 3 | 100% | 4 |
| 8 | COLAK, C , COLAK, MC , ERMIS, N , ERDIL, N , OZDEMIR, R , (2016) PREDICTION OF CHOLESTEROL LEVEL IN PATIENTS WITH MYOCARDIAL INFARCTION BASED ON MEDICAL DATA MINING METHODS.KUWAIT JOURNAL OF SCIENCE. VOL. 43. ISSUE 3. P. 86 -90 | 4 | 25% | 0 |
| 9 | PETERSSON, U , OSTGREN, CJ , BRUDIN, L , OVHED, I , NILSSON, PM , (2008) PREDICTORS OF SUCCESSFUL, SELF-REPORTED LIFESTYLE CHANGES IN A DEFINED MIDDLE-AGED POPULATION: THE SODERAKRA CARDIOVASCULAR RISK FACTOR STUDY, SWEDEN.SCANDINAVIAN JOURNAL OF PUBLIC HEALTH. VOL. 36. ISSUE 4. P. 389-396 | 5 | 24% | 4 |
| 10 | BOG-HANSEN, E , LINDBLAD, U , BENGTSSON, K , RANSTAM, J , MELANDER, A , RASTAM, L , (1998) RISK FACTOR CLUSTERING IN PATIENTS WITH HYPERTENSION AND NON-INSULIN-DEPENDENT DIABETES MELLITUS. THE SKARABORG HYPERTENSION PROJECT.JOURNAL OF INTERNAL MEDICINE. VOL. 243. ISSUE 3. P. 223 -232 | 6 | 24% | 26 |
Classes with closest relation at Level 1 |