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EARLY DETECTION OF ALZHEIMER’S DISEASE USING DATA MINING: COMPARISON OF ENSEMBLE FEATURE SELECTION APPROACHES

Year 2021, Volume: 9 Issue: 1, 50 - 61, 02.03.2021
https://doi.org/10.36306/konjes.731624

Abstract

Early Alzheimer's disease detection has become an important research area for many years.
Various studies in the field of Alzheimer's disease detection have focused on applying individual feature selection methods. In addition to individual feature selection methods, the ensemble feature selection approach has become a creative field. It advocates the combination of the ranked features from various feature selection methods to obtain better results than the current approaches. Thus, this study aims to build a predictive model for early diagnosis of Alzheimer's disease using the ensemble feature selection approaches. Also, Alzheimer's disease dataset consists of three target classes: Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD). In this study, homogeneous and heterogeneous ensemble approaches have been applied in the feature selection process. Two feature subsets are created based on these ensemble feature selection approaches. A predictive model for early diagnosis of Alzheimer's disease has been build applying Random Forest, Artificial Neural Network, Logistic Regression, Support Vector Machine, and Naïve Bayes data mining algorithms. The predictive model uses the two feature subsets applying these algorithms separately. Then, the performance results are compared to determine which ensemble feature selection approach performs better than the other.
This study revealed that better performance result is provided applying Random Forest algorithm with feature subset obtained using the heterogeneous ensemble feature selection approach (91%).

References

  • Ahmed O.B., Mizotin, M., Benois-Pineau, J., Allard, M., Catheline. G., Amar, C. B., Alzheimer's Disease Neuroimaging Initiative., 2015, "Alzheimer's disease diagnosis on structural MR images using circular harmonic functions descriptors on the hippocampus and posterior cingulate cortex", Computerized Medical Imaging and Graphics, Vol. 44, pp. 13-25.
  • Aldehim, G., 2015, Heuristic ensembles of filters for accurate and reliable feature selection, Doctoral dissertation, University of East Anglia.
  • Balakrishnan, D., Puthusserypady, S., “Multilayer perceptrons for the classification of brain-computer interface data”, In Proceedings of the IEEE 31st Annual Northeast Bioengineering Conference, 118-119, 2005.
  • Bansal, D., Chhikara, R., Khanna, K., Gupta, P., 2018, “Comparative analysis of various machine learning algorithms for detecting dementia”, Procedia computer science, 132, 1497-1502.
  • Bhagyashree, S.R., Nagaraj, K., Prince, M., Fall, C.H., Krishna, M., 2018, “Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India”, Social Psychiatry and Psychiatric Epidemiology, Vol. 53, No. 1, pp.77-86.
  • Bhagyashree S.R., Sheshadri H.S., "An initial investigation in the diagnosis of Alzheimer’s disease using various classification techniques", IEEE International Conference on Computational Intelligence and Computing Research, 2014.
  • Bookheimer, S.Y., Strojwas, M.H., Cohen, M.S., Saunders, A.M., Pericak-Vance, M.A., Mazziotta, J.C., Small, G. W., 2000, "Patterns of brain activation in people at risk of Alzheimer's disease", New England journal of medicine, Vol. 343, No. 7, pp.450-456.
  • Campos, S., Pizarro, L., Valle, C., Gray, K.R., Rueckert, D., Allende, H., “Evaluating Imputation Techniques for Missing Data in ADNI: A Patient Classification Study”, In Iberoamerican Congress on Pattern Recognition, 3-10, November 2015.
  • Chaves, R., Ramírez, J., Gorriz, J. M., 2013, "Integrating discretization and association rule-based classification for Alzheimer’s disease diagnosis", Expert Systems with Applications, Vol. 40, No. 5, pp.1571-1578.
  • Chen, R., Herskovits, E.H, 2010, “Machine-learning techniques for building a diagnostic model for very mild dementia”, Neuroimage, Vol. 52, No. 1, pp.234-244.
  • Cuingnet, R., Gerardin, E., Tessieras J., Auzias, G., Lehéricy, S., Habert, M.O., Alzheimer's Disease Neuroimaging Initiative, 2011, “Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database,” NeuroImage, Vol. 56, No. 2, pp.766-781.
  • Dallora, A.L., Eivazzadeh, S., Mendes, E., Berglund, J., Anderberg, P., 2017, “Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review”, PloS one, Vol. 12, No. 6.
  • Escudero, J., Ifeachor, E., Zajicek, J.P., Green, C., Shearer, J., Pearson, S, 2013, “Machine learning-based method for personalized and cost-effective detection of Alzheimer's disease”, IEEE transactions on biomedical engineering, Vol. 60, No. 1, pp.164-168.
  • Farhan, S., Fahiem, M.A., Tauseef, H., 2014, "An Ensemble-of-Classifiers Based Approach for Early Diagnosis of Alzheimer's Disease: Classification Using Structural Features of Brain Images", Computational and mathematical methods in medicine.
  • Farid, A.A., Selim, G., Khater, H., 2020, “Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s disease”.
  • Hand, D.J., 2007, "Principles of data mining", Drug Safety, 30(7), 621-622. Vol. 30, No. 7, pp.621-622.
  • Huang, M., Yang, W., Feng, Q., & Chen, W., 2017, “Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease”. Scientific reports, Vol. 7, No. 1, pp. 1-13.
  • Jo, T., Nho, K., Saykin, A.J., 2019, “Deep Learning in Alzheimer's disease: Diagnostic Classification and Prognostic Prediction using Neuroimaging Data”, Frontiers in aging neuroscience, Vol. 11, pp.272.
  • Khan, A., Usman, M., 2019, “Alzheimer’s Disease Prediction Model Using Demographics and Categorical Data”, International Journal of Online and Biomedical Engineering (iJOE), Vol. 15, No. 15, pp.96-109.
  • Khazaee, A., Ebrahimzadeh, A., Babajani-Feremi, A., 2016, “Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer‘s disease”, Brain imaging and behavior, Vol. 10, No. 3, pp.799-817.
  • Klöppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J.D., Frackowiak, R.S., 2008, “Automatic classification of MR scans in Alzheimer's disease”, Brain, Vol. 131, No. 3, pp.681-689.
  • Kumar, K.R., Vanaja, S., 2014, “Analysis of feature selection algorithms on classification: a survey”.
  • Lama, R.K., Gwak, J., Park, J.S., Lee, S.W., 2017, “Diagnosis of Alzheimer’s disease based on structural MRI images using a regularized extreme learning machine and PCA features”, Journal of healthcare engineering.
  • Lee, G., Nho, K., Kang, B., Sohn, K.A., Kim, D, 2019, “Predicting Alzheimer’s disease progression using multi-modal deep learning approach”, Scientific reports, Vol. 9, No. 1.
  • Little, R.J., Rubin, D.B., 2019, “Statistical analysis with missing data ", John Wiley & Sons, Vol. 793.
  • Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D., “Early diagnosis of Alzheimer's disease with deep learning”, IEEE 11th international symposium on biomedical imaging (ISBI), 1015-1018, 2014.
  • Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I., de Mendonça, A., 2011, “Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests”, BMC research notes, Vol. 4, No. 1, pp.299-
  • Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J., 2015, “Machine learning framework for early MRI- based Alzheimer's conversion prediction in MCI subjects”, Neuroimage, Vol. 104, pp. 398-412.
  • Munteanu, C.R., Fernandez-Lozano, C., Abad, V.M., Fernández, S.P., Álvarez-Linera, J., Hernández- Tamames, J.A., Pazos, A., 2015, “Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using 1 H Magnetic Resonance Spectroscopy data”, Expert Systems with Applications, Vol. 42, No. 15, pp.6205-6214.
  • Muralidharan, S., Phiri, K., Sinha, S.K., Kim, B., 2018, “ANALYSIS AND PREDICTION OF REAL ESTATE PRICES: A CASE OF THE BOSTON HOUSING MARKET”i, Issues in Information Systems, Vol. 19, No. 2, pp. 109-118.
  • Nunes, C., Silva, D., Guerreiro, M., de Mendonça, A., Carvalho, A. M., Madeira, S. C., "Class Imbalance in the Prediction of Dementia from Neuropsychological Data", In Portuguese Conference on Artificial Intelligence, Springer Berlin Heidelberg., 138-151, 2013.
  • Patel, J., Shah, S., Thakkar, P., Kotecha, K., 2015, “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques”, Expert Systems with Applications, Vol. 42, No. 1, pp. 259–268.
  • Patil, V., Shimpi, S., 2011, “Handwritten English character recognition using neural network”, Elixir Comput Sci Eng, Vol. 41, pp. 5587-5591.
  • Quintana, M., Guàrdia, J., Sánchez-Benavides, G., Aguilar, M., Molinuevo, J.L., Robles, A., Fernández, M., 2012, “Using artificial neural networks in clinical neuropsychology: High performance in mild cognitive impairment and Alzheimer‘s disease", Journal of Clinical and Experimental Neuropsychology, Vol. 34, No. 2, pp. 195–208.
  • Sana, B., Siddiqui, I.F., Arain, Q. A., 2019, “Analyzing Students’ Academic Performance through Educational Data Mining”, 3c Tecnología: glosas de innovación aplicadas a la pyme, Vol. 8, No. 29, pp. 402-421.
  • Seijo-Pardo, B., Porto-Díaz, I., Bolón-Canedo, V., Alonso-Betanzos, A, 2017, “Ensemble feature selection: homogeneous and heterogeneous approaches”, Knowledge-Based Systems, Vol. 118, pp. 124-139.
  • Shankle, W.R., Mani, S., Pazzani, M.J., Smyth, P., “Detecting very early stages of dementia from normal aging with machine learning methods”, In Conference on Artificial Intelligence in Medicine in Europe Springer, Berlin, Heidelberg, 71-85, 1997.
  • Stamps, J.J., Bartoshuk, L.M., Heilman, K.M., 2013, “A brief olfactory test for Alzheimer's disease”, Journal of the neurological sciences, Vol. 333, No. 1-2, pp. 19-24.
  • Suk, H.I., Lee, S.W., Shen, D, 2015, “Latent feature representation with stacked auto-encoder for AD/MCI diagnosis.”, Brain Structure and Function, Vol. 220, No. 2, pp. 841–859.
  • Supekar, K, Menon, V, Rubin, D, Musen, M, Greicius M.D, 2008, “Network analysis of intrinsic functional brain connectivity in Alzheimer's disease”, PLoSComputBiol, Vol. 4, No. 6, pp. 1-11.
  • Tang, J., Alelyani, S., Liu, H., 2014, “Feature selection for classification: A review.“, Data classification: Algorithms and applications, 37.
  • Teipel, S.J., Born, C., Ewers M. 2007, “Multivariate deformation based analysis of brain atrophy to predict Alzheimer’s disease in mild cognitive impairment”, NeuroImage, Vol. 38, No. 1, pp. 13–24.
  • Tong, T., Gray, K., Gao, Q., Chen, L., Rueckert, D., 2017, “Multi-modal classification of Alzheimer's disease using nonlinear graph fusion”, Pattern recognition, Vol. 63, pp. 171-181.
  • Trambaiolli, L.R., Spolaôr, N., Lorena, A.C., Anghinah, R., Sato, J.R., 2019, “Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease”, Clinical Neurophysiology, 128(10), 2058-2067. Vol. 128, No. 10, pp. 2058-2067.
  • Vapnik, V.N., 1995, “The Nature of Statistical Learning”, Theory.
  • Wee, C.Y., Yap, P.T., Shen, D., 2013, “Prediction of Alzheimer’s disease and mild cognitive impairment using cortical morphological patterns”, Human Brain Mapping, Vol. 34, No. 12, pp. 3411–3425.
  • Westman, E., Muehlboeck, J.S., Simmons, A, 2012, "Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion.", Neuroimage, Vol. 62, No. 1, pp. 229-238.
  • Williams, J.A., Alyssa W., Diane J.C, Maureen S., "Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia.", In Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, pp.71-76, 2013.
  • Wordoffa, H., Wangoria, E., 2012, "Alzheimer's Disease Stage Prediction using Machine Learning and Multi-Agent System".
  • Zhang, R., Simon, G., Yu, F, 2017, "Advancing Alzheimer's research: A review of big data promises", International Journal of medical informatics, Vol. 106, pp. 48-56.
  • Zhang D, Shen D, 2011, “Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease”, NeuroImage, Vol. 59, No. 2, pp. 895–907.
  • Zhang, Y., Wang, S., Phillips, P., Dong, Z., Ji, G., Yang, J., 2015, “Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA- KSVM trained by PSOTVAC”, Biomedical Signal Processing and Control, Vol. 21, pp. 58–73.
  • Zhao, Y., He, L., “Deep learning in the EEG diagnosis of Alzheimer’s disease. In Asian Conference on Computer Vision”, Springer International Publishing, pp. 340-353, November 2014.

Veri Madenciliği Kullanılarak Alzheimer Hastalığının Erken Tespiti: Topluluk Özellik Seçim Yaklaşımlarının Karşılaştırılması

Year 2021, Volume: 9 Issue: 1, 50 - 61, 02.03.2021
https://doi.org/10.36306/konjes.731624

Abstract

Erken Alzheimer hastalığı tespiti uzun yıllardır önemli bir araştırma alanı haline gelmiştir. Alzheimer hastalığı tespiti alanında yapılan çeşitli çalışmalar, bireysel özellik seçme yöntemlerini uygulamaya odaklanmıştır. Bireysel özellik seçme yöntemlerine ek olarak, topluluk özellik seçme yaklaşımı yaratıcı bir alan haline gelmiştir. Bu yaklaşım, mevcut yaklaşımlardan daha iyi sonuçlar elde etmek için çeşitli özellik seçim yöntemlerinden sıralanan özelliklerin kombinasyonunu savunur. Bu nedenle, bu çalışmanın amacı, topluluk özellik seçim yaklaşımlarını kullanarak Alzheimer hastalığının erken teşhisi için bir öngörücü model oluşturmaktır. Ayrıca, Alzheimer hastalığı veri seti üç hedef sınıftan oluşur: Normal (CN), Hafif Bilişsel Bozukluk (MCI) ve Alzheimer hastalığı (AD). Bu çalışmada, özellik seçim sürecinde homojen ve heterojen topluluk yaklaşımları uygulanmıştır. Bu topluluk özellik seçim yaklaşımlarına dayanarak iki özellik alt kümesi oluşturulmuştur. Rastgele Orman, Yapay Sinir Ağı, Lojistik Regresyon, Destek Vektör Makinesi ve Naïve Bayes veri madenciliği algoritmaları uygulanarak Alzheimer hastalığının erken teşhisi için bir tahmin modeli oluşturulmuştur. Bu tahmin modeli yukarıda bahsedilen algoritmaları her iki özellik alt kümesini de ayrı ayrı kullanarak bir tahminde bulunmuştur. Ardından, hangi topluluk özellik seçim yaklaşımının diğerinden daha iyi performans gösterdiğini belirlemek için performans sonuçları karşılaştırılmıştır. Bu çalışma, heterojen topluluk özellik seçim yaklaşımı kullanılarak elde edilen özellik altkümesi ile Rastgele Orman algoritması uygulanarak daha iyi performans sonucunun sağlandığını ortaya koymuştur (% 91).

References

  • Ahmed O.B., Mizotin, M., Benois-Pineau, J., Allard, M., Catheline. G., Amar, C. B., Alzheimer's Disease Neuroimaging Initiative., 2015, "Alzheimer's disease diagnosis on structural MR images using circular harmonic functions descriptors on the hippocampus and posterior cingulate cortex", Computerized Medical Imaging and Graphics, Vol. 44, pp. 13-25.
  • Aldehim, G., 2015, Heuristic ensembles of filters for accurate and reliable feature selection, Doctoral dissertation, University of East Anglia.
  • Balakrishnan, D., Puthusserypady, S., “Multilayer perceptrons for the classification of brain-computer interface data”, In Proceedings of the IEEE 31st Annual Northeast Bioengineering Conference, 118-119, 2005.
  • Bansal, D., Chhikara, R., Khanna, K., Gupta, P., 2018, “Comparative analysis of various machine learning algorithms for detecting dementia”, Procedia computer science, 132, 1497-1502.
  • Bhagyashree, S.R., Nagaraj, K., Prince, M., Fall, C.H., Krishna, M., 2018, “Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India”, Social Psychiatry and Psychiatric Epidemiology, Vol. 53, No. 1, pp.77-86.
  • Bhagyashree S.R., Sheshadri H.S., "An initial investigation in the diagnosis of Alzheimer’s disease using various classification techniques", IEEE International Conference on Computational Intelligence and Computing Research, 2014.
  • Bookheimer, S.Y., Strojwas, M.H., Cohen, M.S., Saunders, A.M., Pericak-Vance, M.A., Mazziotta, J.C., Small, G. W., 2000, "Patterns of brain activation in people at risk of Alzheimer's disease", New England journal of medicine, Vol. 343, No. 7, pp.450-456.
  • Campos, S., Pizarro, L., Valle, C., Gray, K.R., Rueckert, D., Allende, H., “Evaluating Imputation Techniques for Missing Data in ADNI: A Patient Classification Study”, In Iberoamerican Congress on Pattern Recognition, 3-10, November 2015.
  • Chaves, R., Ramírez, J., Gorriz, J. M., 2013, "Integrating discretization and association rule-based classification for Alzheimer’s disease diagnosis", Expert Systems with Applications, Vol. 40, No. 5, pp.1571-1578.
  • Chen, R., Herskovits, E.H, 2010, “Machine-learning techniques for building a diagnostic model for very mild dementia”, Neuroimage, Vol. 52, No. 1, pp.234-244.
  • Cuingnet, R., Gerardin, E., Tessieras J., Auzias, G., Lehéricy, S., Habert, M.O., Alzheimer's Disease Neuroimaging Initiative, 2011, “Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database,” NeuroImage, Vol. 56, No. 2, pp.766-781.
  • Dallora, A.L., Eivazzadeh, S., Mendes, E., Berglund, J., Anderberg, P., 2017, “Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review”, PloS one, Vol. 12, No. 6.
  • Escudero, J., Ifeachor, E., Zajicek, J.P., Green, C., Shearer, J., Pearson, S, 2013, “Machine learning-based method for personalized and cost-effective detection of Alzheimer's disease”, IEEE transactions on biomedical engineering, Vol. 60, No. 1, pp.164-168.
  • Farhan, S., Fahiem, M.A., Tauseef, H., 2014, "An Ensemble-of-Classifiers Based Approach for Early Diagnosis of Alzheimer's Disease: Classification Using Structural Features of Brain Images", Computational and mathematical methods in medicine.
  • Farid, A.A., Selim, G., Khater, H., 2020, “Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s disease”.
  • Hand, D.J., 2007, "Principles of data mining", Drug Safety, 30(7), 621-622. Vol. 30, No. 7, pp.621-622.
  • Huang, M., Yang, W., Feng, Q., & Chen, W., 2017, “Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease”. Scientific reports, Vol. 7, No. 1, pp. 1-13.
  • Jo, T., Nho, K., Saykin, A.J., 2019, “Deep Learning in Alzheimer's disease: Diagnostic Classification and Prognostic Prediction using Neuroimaging Data”, Frontiers in aging neuroscience, Vol. 11, pp.272.
  • Khan, A., Usman, M., 2019, “Alzheimer’s Disease Prediction Model Using Demographics and Categorical Data”, International Journal of Online and Biomedical Engineering (iJOE), Vol. 15, No. 15, pp.96-109.
  • Khazaee, A., Ebrahimzadeh, A., Babajani-Feremi, A., 2016, “Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer‘s disease”, Brain imaging and behavior, Vol. 10, No. 3, pp.799-817.
  • Klöppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J.D., Frackowiak, R.S., 2008, “Automatic classification of MR scans in Alzheimer's disease”, Brain, Vol. 131, No. 3, pp.681-689.
  • Kumar, K.R., Vanaja, S., 2014, “Analysis of feature selection algorithms on classification: a survey”.
  • Lama, R.K., Gwak, J., Park, J.S., Lee, S.W., 2017, “Diagnosis of Alzheimer’s disease based on structural MRI images using a regularized extreme learning machine and PCA features”, Journal of healthcare engineering.
  • Lee, G., Nho, K., Kang, B., Sohn, K.A., Kim, D, 2019, “Predicting Alzheimer’s disease progression using multi-modal deep learning approach”, Scientific reports, Vol. 9, No. 1.
  • Little, R.J., Rubin, D.B., 2019, “Statistical analysis with missing data ", John Wiley & Sons, Vol. 793.
  • Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D., “Early diagnosis of Alzheimer's disease with deep learning”, IEEE 11th international symposium on biomedical imaging (ISBI), 1015-1018, 2014.
  • Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I., de Mendonça, A., 2011, “Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests”, BMC research notes, Vol. 4, No. 1, pp.299-
  • Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J., 2015, “Machine learning framework for early MRI- based Alzheimer's conversion prediction in MCI subjects”, Neuroimage, Vol. 104, pp. 398-412.
  • Munteanu, C.R., Fernandez-Lozano, C., Abad, V.M., Fernández, S.P., Álvarez-Linera, J., Hernández- Tamames, J.A., Pazos, A., 2015, “Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using 1 H Magnetic Resonance Spectroscopy data”, Expert Systems with Applications, Vol. 42, No. 15, pp.6205-6214.
  • Muralidharan, S., Phiri, K., Sinha, S.K., Kim, B., 2018, “ANALYSIS AND PREDICTION OF REAL ESTATE PRICES: A CASE OF THE BOSTON HOUSING MARKET”i, Issues in Information Systems, Vol. 19, No. 2, pp. 109-118.
  • Nunes, C., Silva, D., Guerreiro, M., de Mendonça, A., Carvalho, A. M., Madeira, S. C., "Class Imbalance in the Prediction of Dementia from Neuropsychological Data", In Portuguese Conference on Artificial Intelligence, Springer Berlin Heidelberg., 138-151, 2013.
  • Patel, J., Shah, S., Thakkar, P., Kotecha, K., 2015, “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques”, Expert Systems with Applications, Vol. 42, No. 1, pp. 259–268.
  • Patil, V., Shimpi, S., 2011, “Handwritten English character recognition using neural network”, Elixir Comput Sci Eng, Vol. 41, pp. 5587-5591.
  • Quintana, M., Guàrdia, J., Sánchez-Benavides, G., Aguilar, M., Molinuevo, J.L., Robles, A., Fernández, M., 2012, “Using artificial neural networks in clinical neuropsychology: High performance in mild cognitive impairment and Alzheimer‘s disease", Journal of Clinical and Experimental Neuropsychology, Vol. 34, No. 2, pp. 195–208.
  • Sana, B., Siddiqui, I.F., Arain, Q. A., 2019, “Analyzing Students’ Academic Performance through Educational Data Mining”, 3c Tecnología: glosas de innovación aplicadas a la pyme, Vol. 8, No. 29, pp. 402-421.
  • Seijo-Pardo, B., Porto-Díaz, I., Bolón-Canedo, V., Alonso-Betanzos, A, 2017, “Ensemble feature selection: homogeneous and heterogeneous approaches”, Knowledge-Based Systems, Vol. 118, pp. 124-139.
  • Shankle, W.R., Mani, S., Pazzani, M.J., Smyth, P., “Detecting very early stages of dementia from normal aging with machine learning methods”, In Conference on Artificial Intelligence in Medicine in Europe Springer, Berlin, Heidelberg, 71-85, 1997.
  • Stamps, J.J., Bartoshuk, L.M., Heilman, K.M., 2013, “A brief olfactory test for Alzheimer's disease”, Journal of the neurological sciences, Vol. 333, No. 1-2, pp. 19-24.
  • Suk, H.I., Lee, S.W., Shen, D, 2015, “Latent feature representation with stacked auto-encoder for AD/MCI diagnosis.”, Brain Structure and Function, Vol. 220, No. 2, pp. 841–859.
  • Supekar, K, Menon, V, Rubin, D, Musen, M, Greicius M.D, 2008, “Network analysis of intrinsic functional brain connectivity in Alzheimer's disease”, PLoSComputBiol, Vol. 4, No. 6, pp. 1-11.
  • Tang, J., Alelyani, S., Liu, H., 2014, “Feature selection for classification: A review.“, Data classification: Algorithms and applications, 37.
  • Teipel, S.J., Born, C., Ewers M. 2007, “Multivariate deformation based analysis of brain atrophy to predict Alzheimer’s disease in mild cognitive impairment”, NeuroImage, Vol. 38, No. 1, pp. 13–24.
  • Tong, T., Gray, K., Gao, Q., Chen, L., Rueckert, D., 2017, “Multi-modal classification of Alzheimer's disease using nonlinear graph fusion”, Pattern recognition, Vol. 63, pp. 171-181.
  • Trambaiolli, L.R., Spolaôr, N., Lorena, A.C., Anghinah, R., Sato, J.R., 2019, “Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease”, Clinical Neurophysiology, 128(10), 2058-2067. Vol. 128, No. 10, pp. 2058-2067.
  • Vapnik, V.N., 1995, “The Nature of Statistical Learning”, Theory.
  • Wee, C.Y., Yap, P.T., Shen, D., 2013, “Prediction of Alzheimer’s disease and mild cognitive impairment using cortical morphological patterns”, Human Brain Mapping, Vol. 34, No. 12, pp. 3411–3425.
  • Westman, E., Muehlboeck, J.S., Simmons, A, 2012, "Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion.", Neuroimage, Vol. 62, No. 1, pp. 229-238.
  • Williams, J.A., Alyssa W., Diane J.C, Maureen S., "Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia.", In Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, pp.71-76, 2013.
  • Wordoffa, H., Wangoria, E., 2012, "Alzheimer's Disease Stage Prediction using Machine Learning and Multi-Agent System".
  • Zhang, R., Simon, G., Yu, F, 2017, "Advancing Alzheimer's research: A review of big data promises", International Journal of medical informatics, Vol. 106, pp. 48-56.
  • Zhang D, Shen D, 2011, “Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease”, NeuroImage, Vol. 59, No. 2, pp. 895–907.
  • Zhang, Y., Wang, S., Phillips, P., Dong, Z., Ji, G., Yang, J., 2015, “Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA- KSVM trained by PSOTVAC”, Biomedical Signal Processing and Control, Vol. 21, pp. 58–73.
  • Zhao, Y., He, L., “Deep learning in the EEG diagnosis of Alzheimer’s disease. In Asian Conference on Computer Vision”, Springer International Publishing, pp. 340-353, November 2014.
There are 53 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Selim Buyrukoğlu

Publication Date March 2, 2021
Submission Date May 4, 2020
Acceptance Date September 29, 2020
Published in Issue Year 2021 Volume: 9 Issue: 1

Cite

IEEE S. Buyrukoğlu, “EARLY DETECTION OF ALZHEIMER’S DISEASE USING DATA MINING: COMPARISON OF ENSEMBLE FEATURE SELECTION APPROACHES”, KONJES, vol. 9, no. 1, pp. 50–61, 2021, doi: 10.36306/konjes.731624.

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