Dergiler / Turkish Journal of Medical Sciences / 2010 / Cilt: 40 - Sayı: 3

An application of nonlinear canonical correlation analysis on medical data

Sayfa
503–510
Yayın Tarihi
2022-11-05
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Conclusions: OVERALS analysis proved to be a quite useful method in graphical expression and interpretation of data structure, revealing similarities and relational structures among multi-dimensional categorical variable sets, which are used often in the field of medicine and their comprehensive interpretation.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In studies in the field of medicine, necessity to examine the relations between data sets composed of categorical variable groups is widely encountered.
📊
Temel Bulgular & Veri:In the study, 10 variables were divided into 3 groups, namely anamnesis, symptoms, and laboratory tests.
💡
Sonuç & Katkı:Conclusions: OVERALS analysis proved to be a quite useful method in graphical expression and interpretation of data structure, revealing similarities and relational structures among multi-dimensional categorical variable sets, which are used often in the field of medicine and their comprehensive interpretation.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Key words: Alternating least squares algorithm (ALS) · homogeneity analysis (HOMALS) · multivariate analysis

Özet

In studies in the field of medicine, necessity to examine the relations between data sets composed of categorical variable groups is widely encountered. In this study, it was aimed to examine nonlinear canonical correlation analysis (OVERALS) method, which allows examination of relations among K number of categorical variable sets and structural similarities of the data set, and to discuss usefulness of the method in more comprehensive data sets obtained from studies carried out in the field of medicine in terms of practice and interpretation. Materials and methods: OVERALS method was applied to a part of data set obtained from a study carried out with diarrhea patients. In the study, 10 variables were divided into 3 groups, namely anamnesis, symptoms, and laboratory tests. In order to examine similarities and relationships among these 3 variable groups, OVERALS method was used and results were expressed with graphical presentations. Results: It was observed that OVERALS analysis allows more detailed presentation of data structure and relations among variable sets. Conclusions: OVERALS analysis proved to be a quite useful method in graphical expression and interpretation of data structure, revealing similarities and relational structures among multi-dimensional categorical variable sets, which are used often in the field of medicine and their comprehensive interpretation.

Abstract

In studies in the field of medicine, necessity to examine the relations between data sets composed of categorical variable groups is widely encountered. In this study, it was aimed to examine nonlinear canonical correlation analysis (OVERALS) method, which allows examination of relations among K number of categorical variable sets and structural similarities of the data set, and to discuss usefulness of the method in more comprehensive data sets obtained from studies carried out in the field of medicine in terms of practice and interpretation. Materials and methods: OVERALS method was applied to a part of data set obtained from a study carried out with diarrhea patients. In the study, 10 variables were divided into 3 groups, namely anamnesis, symptoms, and laboratory tests. In order to examine similarities and relationships among these 3 variable groups, OVERALS method was used and results were expressed with graphical presentations. Results: It was observed that OVERALS analysis allows more detailed presentation of data structure and relations among variable sets. Conclusions: OVERALS analysis proved to be a quite useful method in graphical expression and interpretation of data structure, revealing similarities and relational structures among multi-dimensional categorical variable sets, which are used often in the field of medicine and their comprehensive interpretation.

🔖

Bu Makaleye Atıf Yap

ANKARALI, H., ÖĞÜŞ, E., YAZICI, A. C., GÜRBÜZ, F. (2022). An application of nonlinear canonical correlation analysis on medical data. Turkish Journal of Medical Sciences, Cilt: 40 - Sayı: 3, 503–510. https://ajindex.com/articles/611542

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (8)

  1. Andersen EB. The statistical analysis of categorical data. Germany: Springer Verlag; 1990. Greenacre MJ. Practical Correspondence Analysis. In Barnett V, editors, Interpreting Multivariate Data. UK: John Wiley and Sons; 1981. p. 119-146.
  2. Greenacre MJ, Underhill LG. Scaling a Data Matrix in a Low Dimensional Euclidean Space. In: Hawkins DM, editors. Topics in Applied Multivariate Analysis. USA: Cambridge University Press; 1982. p. 183-267.
  3. Greenacre M, Hastie T. The geometric interpretation of correspondence analysis. Journal of the American Statistical Association 1987; 82: 437-447.
  4. Greenacre, M. (1993). Correspondence Analysis in Practice. London: Academic Press. Greenacre M. Correspondence Analysis and its Interpretation. In Greenacre M, Blasius J, editors. Correspondence Analysis in the Social Sciences. London: Academic Press, Harcourt Brace&Company Publishers; 1994. p. 3-22.
  5. Blasius J, Greenacre M. Computation of correspondence analysis. In Greenacre M, Blasius J, editors. Correspondence analysis in the social sciences London: Academic Press 1994. p. 53-78.
  6. Carroll JD, Green PE. An INDSCAL-Based approach to multiple correspondence analyses. Journal of Marketing Research 1988; 25: 193-203.
  7. Cui Y, Zhou M, Wong WH. Integrated analysis of microarray data and gene function information. OMICS 2004; 8(2): 106- 17. De Leeuw J, Van der Heijden P. Correspondence analysis of incomplete contingency tables. Psychometrica 1988; 53(2): 223- 233. Escofier B, Pages J. Presentation of correspondence analysis and multiple correspondence analyses with the help of examples. In Devillers J, Karcher W, editors. Applied Multivariate Analysis in SAR and Environmental Studies. Netherlands: Kluwer Academic Pub; 1991. p. 1-31. Gower JC. Fisher’s optimal scores and multiple correspondence analysis. Biometrics 1990; 46: 947-961. Greenacre MJ. Correspondence analysis of multivariate categorical data by weighted least-squares. Biometrica 1988; 75(3): 457-467.
  8. Van der Heijden PGM, Mooijaart A, Takane Y. Correspondence Analysis and Contingency Table Models. In: Greenacre M, Blasius J, editors. Correspondence Analysis in the Social Sciences London: Academic Press, Harcourt Brace&Company Publishers; 1994. p. 79-111. Van de Velden M. Dual Scaling and Correspondence Analysis of Rank Order Data. In: Heijmans RDH, Pollock DSG, Satorra A, editors. Innovations in Multivariate Statistical Analysis. Boston: Kluwer Academic Publishers; 2000. p. 87-97. Van de Geer JP. Algebra and Geometry of OVERA