Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Human Action Recognition Using Trajectory-Based Spatiotemporal Descriptors Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Data Mining Approach to Predict and Analyze the Cardiovascular Disease

verfasst von : Anurag Bhatt, Sanjay Kumar Dubey, Ashutosh Kumar Bhatt, Manish Joshi

Erschienen in: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper presents the experimental analysis of data provided by UCI machine learning repository. Weka open source machine learning tool provided by Waikato University reveals the hidden fact behind the datasets on applying supervised mathematical proven algorithm, i.e., J48 and Naïve Bayes algorithm. J48 is an extension of ID3 algorithm having additional features like continuous attribute value ranges and derivation of rules. The data sets were analyzed using two approaches, i.e., first taken with selected attributes and taken with all attributes. The performance of both the algorithm reveals the accuracy of algorithm and predicting the various reasons behind this increasing problem of cardiovascular diseases.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Text Document Classification with PCA and One-Class SVM
Nächstes Kapitel A Hybrid Genetic Algorithm for Cell Formation Problems Using Operational Time
Literatur
1.
Patil, R.R.: Heart Disease Prediction system using Naïve Bayes and Jelinek mercer smoothing, International Journal of Advance Research in Computer and Communication Engineering, ISSN: 2278-1021, Volume 3, Issue 5, pp. 6787–6792, May (2014).
2.
Taneja, A.: Heart Disease Prediction System Using Data Mining Techniques, Oriental Journal of Computer Science and technology, Vol. 6, No. 4, pp. 457–466, December (2013).
3.
Aljumah, A.A., Ahamad, M.G., Siddiqui, M.K.: Application of data mining: Diabetes health care in young and old patients, Journal of King Saud University – Computer and Information Sciences, pp. 127–136, (2013).
4.
Pandey, A.K., Pandey, P., Jaiswal, K.L., Sen, A.K.: Data Mining Clustering Techniques in the Prediction of Heart Disease using Attribute selection Method, International Journal of Science, Engineering and Technology Research (IJSETR), ISSN:2278-7798, Volume 2, Issue 10, pp. 2003–2008, October (2013).
5.
Manikantan, V., Latha, S.: Predicting the Analysis of Heart Disease Symptoms Using Medical Data Mining methods, International Journal on Advanced Computer Theory and Engineering (IJACTE), ISSN: 2319-2526, Volume-2, Issue-2, pp. 5–10, (2013).
6.
Sudhakar, K., Manimekalai, M.: Study of Heart Disease Prediction using Data Mining, International Journal of Advance Research in Computer Science and Software Engineering, Vol. 4, Issue 1, ISSN:2277 128X, pp. 1157–1160, January (2014).
7.
Somanchi, S., Adhikari, S., Lin, A., Eneva, E., Ghani, R.: Early Prediction of Cardiac Arrest (Code Blue) using Electronic Medical Records, ACM, ISBN:978-1-4503-3664, pp. 2119–2126, August 11–14, (2015).
8.
Sathawane, N.K.S., Kshirsagar, P.: Prediction and Analysis of ECG Signal Behaviour using Soft Computing, International Journal of Research in Engineering & Technology, Vol. 2, Issue 5, pp. 199, May (2014).
9.
Palaniappan, S., Awang, R.: Intelligent Heart Disease Prediction System Using Data Mining Techniques, IEEE, pp. 108–115, (2008).
10.
Rani, K.U.: Analysis of Heart Disease Dataset using Neural Network Approach, International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol.1, No.5, pp. 1–8, September (2011).
11.
Murty, N., M., Devi, S., V.: Pattern Recognition: An Algorithmic Approach, ISBN 0857294946 (2011).
12.
Nayak, S.R., Dash, B.K., Mishra, S., Bhutada, T., Jena, M.K.: Sudden Cardiac death in Young Adults, J Indian Acad. forensic Med., ISSN: 0971-0973, Volume 37, No. 4, pp. 438–440, October–December (2015).
13.
Jabbar, M.A., Chandra, P., Deekshatulu, B.L.: Heart Disease Prediction System using Associative Classification and Genetic Algorithm, International Conference on Emerging Trends in Electrical, Electronics and Communication technologies-ICECIT, (2012).
14.
Kaur, B., Singh, W.: Review on Heart Disease Prediction System using Data Mining Techniques, International Journal on Recent and Innovation trends in computing and Communication, ISSN: 2321-8169, Volume 2, Issue 10, pp. 3003–3008, October (2014).
15.
Karthiga, G., Preethi, C., Devi, R.D.H.: Heart Disease Analysis System using Data Mining Techniques, International Journal of Innovative research in Science, Engineering and Technology, ISSN: 2319-8753, Volume 3, Special Issue 3, pp. 3101–3105, March (2014).
16.
Chaurasia, V., Pal, S.: Early Prediction of Heart disease using Data Mining Techniques, Caribbean Journal of Science and Technology, ISSN: 0799-3757, Volume 1, pp. 208–217, (2013).
17.
Srinivas, K., Rao, G.R., Govardhan, A.: Analysis of Attribute Association in Heart Disease using Data Mining Techniques, International Journal of Engineering Research and Applications (IJERA), ISSN:2248-9622, Volume 2, Issue 4, pp. 1680-1683, July–August (2012).
18.
Masethe, H.D., Masethe, M.A.: Prediction of Heart Disease using Classification Algorithms, Proceedings of the World Congress on Engineering and Computer Science, ISSN: 2078-0966, San Francisco, USA, WCECS 2014, pp. 22–24 October (2014).
19.
Ebrahimzadeh, E., Pooyan, M.: Early Detection of Sudden Cardiac Death by using Classical Linear techniques and Time-Frequency methods on Electrocardiogram signals, Journal of Biomedical science and Engineering (JBISE), pp. 699–706, (2011).
20.
Sundar, N.A., Latha, P.P., Chandra, M.R.: Performance Analysis of Classification Data Mining Techniques over Heart Disease Database, International Journal of Engineering Science and Advanced Technology, ISSN: 2250-3676, Vol-2, Issue-3, pp. 470–478, May–June (2012).
21.
Chitra, R., Seenivasagan, V.:Review of Heart Disease prediction system using Data Mining and Hybrid Intelligent techniques, ICTACT Journal on Soft Computing, ISSN:2229-6956, Vol.03, Issue-04, pp. 605–609, July (2013).
22.
Aljumah, A.A., Ahamad, M.G., Siddiqui, M.K.: Application of Data Mining: Diabetes Health Care in Young and Old Patients, Journal of King Saud University-Computer and Information Sciences, pp. 127–136, (2013).
23.
Soni, J., Ansari, U., Sharma, D.,Soni, S.: Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction, International Journal of Computer Applications (0975-8887), Volume 17-No.8, pp. 43–48, March (2011).
Metadaten
Titel
Data Mining Approach to Predict and Analyze the Cardiovascular Disease
verfasst von
Anurag Bhatt
Sanjay Kumar Dubey
Ashutosh Kumar Bhatt
Manish Joshi
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Print ISBN: 978-981-10-3152-6

Electronic ISBN: 978-981-10-3153-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-3153-3_12

Premium Partner

    Bildnachweise