-
Bhatt S., Gething P.W., Brady O.J., Messina J.P., Farlow A.W., Moyes
C.L. et.al. (2016). The global distribution and burden of dengue.
Nature, 496, 504-507.
-
Brady O.J., Gething P.W., Bhatt S., Messina J.P., Brownstein J.S.,
Hoen A.G. et al. (2012). Refining the global spatial limits of dengue
virus transmission by evidence-based consensus. PLoS Negl Trop Dis.
6:1760. doi:10.1371/journal.pntd.0001760
-
Gubler D.J. (1998). Dengue and dengue hemorrhagic fever. Clinical
Microbiology Reviews, 11(3):480–496.
-
Gupta E., Dar L., Kapoor G., Broor S. (2006). The changing
epidemiology of dengue in Delhi, India. Virology Journal, 3: 92.
-
Hati A.K. (2006). Studies on dengue and dengue hemorrhagic fever (DHF)
in West Bengal State, India. Journal of Communicable Diseases, 38(
2):124–129.
-
Sengur A. (2008). An expert system based on linear discriminant
analysis and adaptive neuro-fuzzy inference system to diagnosis heart
valve diseases. Expert Systems with Applications, 35 (1–2), 214–222.
-
Sengur A. (2008). An expert system based on principal component
analysis, artificial immune system and fuzzy k-NN for diagnosis of
valvular heart diseases. Computers in Biology and Medicine, 38 (3)
329–338.
-
Hsu C-C., Ho C-S. (2004). A new hybrid case-based architecture for
medical diagnosis. Information Sciences, 166, 231-247.
-
Das R., Turkoglu I., Sengur A. (2009). Effective diagnosis of heart
disease through neural networks ensembles. Expert Systems with
Applications, 36:7675:7680.
-
Yan H., Jiang Y., Zheng J., Peng C. , Li Q. (2006). A multilayer
perceptron-based medical decision support system for heart disease
diagnosis. Expert Systems with Applications, 30:272 –281.
-
Orhan Er., Yumusak N., Temurtas F. (2012). Diagnosis of chest diseases
using artificial immune system. Expert Systems with Applications, 39(
2):1862–1868.
-
Karabatak M., Cevdet Ince M. (2009). An expert system for detection of
breast cancer based on association rules and neural network. Expert
Systems with Applications, 36: 3465–3469.
-
Orhan Er., Temurtas F., Cetin Tanrıkulu A. (2010). Tuberculosis
Disease Diagnosis Using Artificial Neural Networks. J Med Syst,
34:299–302.
-
Giri, D., Acharya, U. R., Martis, R. J., Sree, S. V., Lim, T. C.,
Ahamed, T.,Suri, J. S. (2013). Automated diagnosis of coronary artery
disease affected patients using LDA, PCA, ICA and discrete wavelet
transform. Knowledge-based Systems, 37, 274-282.
-
Babaoglu, I., Findik, O., Ulker, E. (2010). A comparison of feature
selection models utilizing binary particle swarm optimization and
genetic algorithm in determining coronary artery disease using support
vector machine. Expert Systems with Applications, 37(4), 3177-3183.
-
Patil, B. M., Joshi, R. C., Toshniwal, D. (2010). Hybrid prediction
model for type-2 diabetic patients. Expert systems with applications,
37(12), 8102-8108.
-
Calisir, D., & Dogantekin, E. (2011). A new intelligent hepatitis
diagnosis system: PCA–LSSVM. Expert Systems with Applications, 38(8),
10705-10708.
-
Zheng, B., Yoon, S. W., Lam, S. S. (2014). Breast cancer diagnosis
based on feature extraction using a hybrid of K-means and support
vector machine algorithms. Expert Systems with Applications, 41(4),
1476-1482.
-
Ucar, T., Karahoca, A., Karahoca, D. (2013). Tuberculosis disease
diagnosis by using adaptive neuro fuzzy inference system and rough
sets. Neural Computing and Applications, 23(2), 471-483.
-
Uğuz, H. (2012). Adaptive neuro-fuzzy inference system for diagnosis
of the heart valve diseases using wavelet transform with entropy.
Neural Computing and Applications, 21(7), 1617-1628.
-
Muthukaruppan, S., Er, M. J. (2012). A hybrid particle swarm
optimization based fuzzy expert system for the diagnosis of coronary
artery disease. Expert Systems with Applications, 39(14), 11657-11665.
-
Seera, M., Lim, C. P. (2014). A hybrid intelligent system for medical
data classification. Expert Systems with Applications, 41 (5),
2239-2249.
-
Ubeyli, E. D. (2007). Implementing automated diagnostic systems for
breast cancer detection. Expert Systems with Applications, 33 (4),
1054-1062.
-
Kumar, Y., Sahoo, G. (2013). Prediction of different types of liver
diseases using rule based classification model. Technology and Health
Care, 21 (5): 417-432.
-
Sahoo, Anoop J., Kumar Y. (2014). Seminal quality prediction using
data mining methods. Technology and Health Care, 22 (4): 531-545.
-
Yadav, G., Kumar, Y., Sahoo, G. (2012). Prediction of Parkinson’s
disease using data mining methods: A comparative analysis of tree,
statistical and support vector machine classifiers. In Computing and
Communication Systems (NCCCS), IEEE National Conference, 1-8.
-
Karabatak M., Cevdet Ince M. (2009). An expert system for detection of
breast cancer based on association rules and neural network. Expert
Systems with Applications, 36: 3465–3469.
-
Orhan Er., Temurtas F., Cetin Tanrıkulu A. (2010). Tuberculosis
Disease Diagnosis Using Artificial Neural Networks”, J Med Syst.,
34:299–302.
-
Ripley, B. D. (1996). Pattern recognition and neural networks.
Cambridge University Press.
-
Haykin, S. (1998). Neural networks: A comprehensive foundation,
Englewoods Cliffs. NJ: Prentice-Hall.
-
Bishop, C. M. (2005). Neural networks for pattern recognition. Oxford
Univ Pr.
-
Rumelhart, D. E., Hinton, G. E., Williams, R. J. (1986). Learning
representations by back-propagating errors. Nature, 323, 533–536.
-
Kennedy, J. (2011). Particle swarm optimization. In Encyclopedia of
machine learning. Springer US, 760-766.
-
C. Schaffer (1993). Selecting a classification method by cross
validation. Machine Learning, 13:135–143.
-
Kohavi R. (1995). A study of cross-validation and bootstrap for
accuracy estimation and model selection. In Proceedings of
International Joint Conference on AI, 1137 –1145.