Skip to main content
Erschienen in: Neural Computing and Applications 1/2018

16.07.2016 | Original Article

Diagnosis of Alzheimer's disease using Naive Bayesian Classifier

verfasst von: S. R. Bhagya Shree, H. S. Sheshadri

Erschienen in: Neural Computing and Applications | Ausgabe 1/2018

Einloggen

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

search-config
loading …

Abstract

In the world of modern medicine, though there is lot of medical achievements, some diseases still continue to pest the human race. Unfortunately, dementia is one such disease. All over the world, a large number of people are suffering from dementia. Dementia is a brain-related disease. Diagnosis of the disease at the earlier stage is the requirement of the day. Alzheimer's disease (AD) is one of the types of the dementia, and around 60 % of demented are affected from Alzheimer’s disease (Salmon and Bondi in Neuro psychological assessment of dementia. National Institutes of Health, 2010). All over the world, there are around 35 million people suffering from AD and this number is expected to double by 2030 and more than triple by 2050, that is to 115 million (Prince et al. in World Alzheimer report 2013: journey of caringan analysis of long-term care for dementia. Kings College, London, 2013). Diagnosis of this disease at an early stage will help the patients to lead a quality life for the remaining tenure of their life. In this paper, the authors have collected data of 466 subjects by conducting neuropsychological tests. The authors focus on diagnosis of AD for neuropsychological tests using Naive Bayes.

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

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!

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!

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!

Literatur
2.
Zurück zum Zitat Viswanathan A, Rocca WA, Tzourio C (2009) Vascular risk factors and dementia: how to move forward? Neurology 72:368–374CrossRef Viswanathan A, Rocca WA, Tzourio C (2009) Vascular risk factors and dementia: how to move forward? Neurology 72:368–374CrossRef
3.
Zurück zum Zitat Luck T et al (2010) Incidence of mild cognitive impairment: a systematic review. J Dement Geriatr Cogn Disord 29:P164175 Luck T et al (2010) Incidence of mild cognitive impairment: a systematic review. J Dement Geriatr Cogn Disord 29:P164175
4.
Zurück zum Zitat Thies W, Bleiler L (2013) Alzheimer’s facts and figures, Alzheimer's dement. J Alzheimer’s Assoc Thies W, Bleiler L (2013) Alzheimer’s facts and figures, Alzheimer's dement. J Alzheimer’s Assoc
6.
Zurück zum Zitat Bhagya Shree SR, Sheshadri HS, Joshi S (2014) A review on the method of diagnosing Alzheimer’s disease using data mining. Int J Eng Res Technol 3(3):2417–2420 (ISSN: 2278-0181) Bhagya Shree SR, Sheshadri HS, Joshi S (2014) A review on the method of diagnosing Alzheimer’s disease using data mining. Int J Eng Res Technol 3(3):2417–2420 (ISSN: 2278-0181)
7.
Zurück zum Zitat Bhagyashree SR, Sheshadri HS (2014) An approach in the diagnosis of Alzheimer disease—a survey. Int J Eng Trends Technol (IJETT) 7(1):41–43 (ISSN: 2231-5381) CrossRef Bhagyashree SR, Sheshadri HS (2014) An approach in the diagnosis of Alzheimer disease—a survey. Int J Eng Trends Technol (IJETT) 7(1):41–43 (ISSN: 2231-5381) CrossRef
8.
Zurück zum Zitat Sosa AL et al (2009) Population normative data for the 10/66 Dementia Research Group cognitive test battery from Latin America, India and China: across-sectional survey. BMC Neurol 9:1–11CrossRef Sosa AL et al (2009) Population normative data for the 10/66 Dementia Research Group cognitive test battery from Latin America, India and China: across-sectional survey. BMC Neurol 9:1–11CrossRef
9.
Zurück zum Zitat Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques, 3rd edn. Elsevier, AmsterdamMATH Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques, 3rd edn. Elsevier, AmsterdamMATH
10.
Zurück zum Zitat Soman KP et al (2012) Insight into data mining theory and concepts, 6th edn. PHI Learning Pvt. Ltd, New Delhi Soman KP et al (2012) Insight into data mining theory and concepts, 6th edn. PHI Learning Pvt. Ltd, New Delhi
11.
Zurück zum Zitat Carnero-Pardo C, Espejo-Martnez B, Lopez-Alcalde S, Espinosa-Garca M, Saez-Zea C, Hernandez-Torres E, Navarro-Espigares JL, Vlchez-Carrillo R (2011) Diagnostic accuracy, effectiveness and cost for cognitive impairment and dementia screening of three short cognitive tests applicable to illiterates. PLoS One 6(11):1–6 Carnero-Pardo C, Espejo-Martnez B, Lopez-Alcalde S, Espinosa-Garca M, Saez-Zea C, Hernandez-Torres E, Navarro-Espigares JL, Vlchez-Carrillo R (2011) Diagnostic accuracy, effectiveness and cost for cognitive impairment and dementia screening of three short cognitive tests applicable to illiterates. PLoS One 6(11):1–6
12.
Zurück zum Zitat Williams JA, Weakley A, Cook DJ, Schmitter-Edgecombe M (2013) Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia. In: Workshops at the association for the advancement of artificial intelligence conference in expanding the boundaries of health informatics using artificial intelligence, pp 71–76 Williams JA, Weakley A, Cook DJ, Schmitter-Edgecombe M (2013) Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia. In: Workshops at the association for the advancement of artificial intelligence conference in expanding the boundaries of health informatics using artificial intelligence, pp 71–76
13.
Zurück zum Zitat Schmitter-Edgecombe M, Parsey C, Cook DJ (2011) Cognitive correlates of functional performance in older adults: comparison of self report, direct observation and performance based measures. J Int Neuropsychol Soc 17:853–864CrossRef Schmitter-Edgecombe M, Parsey C, Cook DJ (2011) Cognitive correlates of functional performance in older adults: comparison of self report, direct observation and performance based measures. J Int Neuropsychol Soc 17:853–864CrossRef
14.
Zurück zum Zitat Schmitter-Edgecombe M et al (2009) Characterizing multiple memory deficits and their relation to everyday functioning in individuals with mild cognitive impairment. Neuropsychology 23:168–177CrossRef Schmitter-Edgecombe M et al (2009) Characterizing multiple memory deficits and their relation to everyday functioning in individuals with mild cognitive impairment. Neuropsychology 23:168–177CrossRef
15.
Zurück zum Zitat Shankle WR, Datta P, Dillencourt M, Pazzani M (1996) Improving dementia screening tests with machine learning. Alzheimer’s Res 2(714):95–99 Shankle WR, Datta P, Dillencourt M, Pazzani M (1996) Improving dementia screening tests with machine learning. Alzheimer’s Res 2(714):95–99
16.
Zurück zum Zitat Datta P, Shankle WR, Pazzani M (1996) Applying machine learning to an Alzheimer’s database. In: Conference proceedings of the AAAI symposium Datta P, Shankle WR, Pazzani M (1996) Applying machine learning to an Alzheimer’s database. In: Conference proceedings of the AAAI symposium
17.
Zurück zum Zitat Joshi S, Shenoy PD, Venugopal KR, Patnaik LM (2010) Classification of neuro degenerative disorders based on major risk factors employing machine learning techniques. IACSIT Int J Eng Technol 2(4):350–355 (ISSN:1793-8236) CrossRef Joshi S, Shenoy PD, Venugopal KR, Patnaik LM (2010) Classification of neuro degenerative disorders based on major risk factors employing machine learning techniques. IACSIT Int J Eng Technol 2(4):350–355 (ISSN:1793-8236) CrossRef
18.
Zurück zum Zitat Joshi S, Shenoy PD, Venugopal KR, Patnaik LM (2009) Evaluation of different stages of dementia employing neuropsychological and machine learning techniques. In: First international conference on advanced computing, pp 154–160 Joshi S, Shenoy PD, Venugopal KR, Patnaik LM (2009) Evaluation of different stages of dementia employing neuropsychological and machine learning techniques. In: First international conference on advanced computing, pp 154–160
19.
Zurück zum Zitat Mani S, Dick MB, Pazzani MJ, Teng EL, Kempler D, Taussig IM (1999) Refinement of neuro-psychological tests for dementia screening in a cross cultural population using machine learning. Artif Intell Med 1620:326–335CrossRef Mani S, Dick MB, Pazzani MJ, Teng EL, Kempler D, Taussig IM (1999) Refinement of neuro-psychological tests for dementia screening in a cross cultural population using machine learning. Artif Intell Med 1620:326–335CrossRef
20.
Zurück zum Zitat Bin Othman MF, Yau TMS (2007) Comparison of different classification techniques using WEKA for breast cancer. In: 3rd Kuala Lumpur international conference on biomedical engineering 2006. Springer, Berlin, Heidelberg, pp 520–523CrossRef Bin Othman MF, Yau TMS (2007) Comparison of different classification techniques using WEKA for breast cancer. In: 3rd Kuala Lumpur international conference on biomedical engineering 2006. Springer, Berlin, Heidelberg, pp 520–523CrossRef
21.
Zurück zum Zitat Singhal S, Jena M (2013) A study on WEKA tool for data preprocessing, classification and clustering. Int J Innov Technol Explor Eng (IJITEE) 2(6):250–253 Singhal S, Jena M (2013) A study on WEKA tool for data preprocessing, classification and clustering. Int J Innov Technol Explor Eng (IJITEE) 2(6):250–253
22.
Zurück zum Zitat Andreeva P, Dimitrova M, Radeva P (2004) Data mining learning models and algorithms for medical applications. In: Proceedings of the 18th conference on Saer, pp 11–18 Andreeva P, Dimitrova M, Radeva P (2004) Data mining learning models and algorithms for medical applications. In: Proceedings of the 18th conference on Saer, pp 11–18
23.
Zurück zum Zitat Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Publishers Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Publishers
Metadaten
Titel
Diagnosis of Alzheimer's disease using Naive Bayesian Classifier
verfasst von
S. R. Bhagya Shree
H. S. Sheshadri
Publikationsdatum
16.07.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2416-3

Weitere Artikel der Ausgabe 1/2018

Neural Computing and Applications 1/2018 Zur Ausgabe

Recent advances in Pattern Recognition and Artificial Intelligence

A fast and accurate method for detecting fingerprint reference point

Recent advances in Pattern Recognition and Artificial Intelligence

Sparse sample self-representation for subspace clustering

Recent advances in Pattern Recognition and Artificial Intelligence

Adaptively stepped SPH for fluid animation based on asynchronous time integration