Research Article
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ANN BASED EARLY DETECTION OF ALZHEIMER DISEASE ON SELECTED FEATURES

Year 2023, Volume: 11 Issue: 4, 1508 - 1516, 30.12.2023

Seyit Gazi Yıldız Kazım Yıldız

https://doi.org/10.21923/jesd.1296283

Abstract

Alzheimer's Disease (AD) is a type of dementia, also called cognitive impairment. In cases where measures are not taken against the disease, it may result in a decrease in the quality of life of the person and result in very serious consequences. While it presents with neurological consequences such as decreased functions of thinking and memory, it may result in death in advanced cases. The fact that the treatment is not completely possible makes the place of early diagnosis and intervention important for AD. As a result of the researches carried out in the study, it was seen that there are many studies and scientific content within the framework of AD. A method for early diagnosis of the disease was evaluated by using an open source shared dataset, which includes some disease-specific values and demographic characteristics. By using Artificial Neural Networks (ANN) model, which is one of the machine learning methods, it is aimed to be useful for other studies to take precautions for early detection of the disease. With the ANN, which was classified as dementia and non-dementia individuals, Root Mean Square Error (RMSE) value 0.2302, Mean Absolute Error (MAE) value 0.1899 and accuracy rate of 98.5% was obtained.

Keywords

Alzheimer Disease ANN Artificial Neural Networks Machine Learning Mild Cognitive Impairment.

References

  • Acharya, S. 2021. What are RMSE and MAE?. https://towardsdatascience.com/what-are-rmse-and-mae-e405ce230383 (Access Date: 22.08.2023).
  • Aljović, A., Badnjević, A., Gurbeta, L. 2016. Artificial neural networks in the discrimination of Alzheimer's disease using biomarkers data. In 2016 5th Mediterranean Conference on Embedded Computing (MECO), 12-16 June, Bar, 286-289.
  • Buyrukoğlu, S. 2021. Early Detection of Alzheimer’s Disease Using Data Mining: Comparison of Ensemble Feature Selection Approaches. Konya Mühendislik Bilimleri Dergisi, 9(1), 50-61.
  • Chai, T., Draxler, R. R. 2014. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247-1250.
  • Delikanlı Akbay, G. 2019. Alzheimer Hastalığında B12 Vitamini Eksikliği. Cumhuriyet Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi, 4(3), 22-28.
  • Dinçer, B. 2018. Alzheimer Features. https://www.kaggle.com/datasets/brsdincer/alzheimer-features (Access Date: 01.12.2022).
  • Evyapan Akkuş, D., Güler, A. 2016. Ege Agrafi Test Bataryası'nın Hafif Bilişsel Bozukluk ve Alzheimer Hastalığı Olgularındaki Yazma Bozukluklarını Belirlemedeki Yeri. Türk Psikiyatri Dergisi, 27(3), 185-194.
  • Falahati, F., Westman, E., Simmons, A. 2014. Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging. Journal of Alzheimer's Disease, 41(3), 685-708.
  • Gerontogianni, L. 2022. Dates-Fruit-classification---PyTorch. https://github.com/Lina-Gerontogianni/Dates-Fruit-classification---PyTorch (Access Date: 01.12.2022).
  • Işık, İ. 2022. Classification of Alzheimer Disease with Molecular Communication Systems using LSTM. International Journal of Computational and Experimental Science and Engineering, 8(2), 25-31.
  • Karabay, G. S., Çavaş, M. 2022. Derin öğrenme Yöntemiyle Alzheimer Hastalığının Tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(2), 879-887
  • Kızrak, A. 2018. Şu Kara Kutuyu Açalım: Yapay Sinir Ağları. https://ayyucekizrak.medium.com/%C5%9Fu-kara-kutuyu-a%C3%A7alim-yapay-sinir-a%C4%9Flar%C4%B1-7b65c6a5264a (Access Date: 12.12.2022).
  • Klumpp, P., Fritsch, J., Nöth, E. 2018. ANN-based Alzheimer's disease classification from bag of words. In Speech Communication; 13th ITG-Symposium, 10-12 October, Oldenburg, 1-4.
  • Kour, H., Manhas, J., & Sharma, V. 2019. Evaluation of adaptive neuro-fuzzy inference system with artificial neural network and fuzzy logic in diagnosis of Alzheimer disease. In 2019 6th International conference on computing for sustainable global development (INDIACom), 13-15 March, New Delhi, 1041-1046.
  • Köseoğlu, B. 2021. Model Performansını Değerlendirmek: Regresyon. https://medium.com/yaz%C4%B1l%C4%B1m-ve-bili%C5%9Fim-kul%C3%BCb%C3%BC/model-performans%C4%B1n%C4%B1-de%C4%9Ferlendirmek-regresyon-48b4afec8664 (Access Date: 23.08.2023).
  • Mahajan, S., Bangar, G., Kulkarni, N. 2020. Machine Learning Algorithms for Classification of Various Stages of Alzheimer's Disease: A Review. Machine Learning, 7(08), 817-824.
  • Nancy Noella, R. S., Priyadarshini, J. 2020. Diagnosis of Alzheimer’s and Parkinson’s disease using artificial neural network. Int J Sci Technol Res, 9(3), 3659-3664.
  • Neelaveni, J., Devasana, M. S. G. 2020. Alzheimer Disease Prediction using Machine Learning Algorithms. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 6-7 March, Coimbatore, 101-104.
  • Özkaya, A., Cebeci, U. 2022. A Model Suggestion For Alzheimer’s Disease Diagnosis By Using Deep Learning. Avrupa Bilim ve Teknoloji Dergisi, (37), 123-130.
  • Salunkhe, S. Y., Chavan, M. S. 2022. Prediction of Alzheimer's disease using Machine Learning Algorithm. In 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), 26-28 January, Chiang Rai, 400-405.
  • Sertkaya, M. E., Ergen, B. 2022. Alzheimer Hastalığının Erken Teşhisinin Çoklu Değişken Kullanarak Tespiti. Avrupa Bilim ve Teknoloji Dergisi, (35), 306-314.
  • Sun, D., Peng, H., & Wu, Z. 2022. Establishment and analysis of a combined diagnostic model of alzheimer's disease with random forest and artificial neural network. Frontiers in Aging Neuroscience, 14, 921906.
  • Tufail, A. B., Abidi, A., Siddiqui, A. M., Younis, M. S. 2012. Automatic Classification of Initial Categories of Alzheimer's Disease from Structural MRI Phase Images: A Comparison of PSVM, KNN and ANN Methods. International Journal of Biomedical and Biological Engineering, 6(12), 713-717.
  • Quintana, M., Guàrdia, J., Sánchez-Benavides, G., Aguilar, M., Molinuevo, J. L., Robles, A., Barquero, M. A., Antúnez, C., Martínez-Parra, C., Frank-García, A., Fernández, M., Blesa, R., Peña-Casanova, J., Neuronorma Study Team 2012. Using artificial neural networks in clinical neuropsychology: High performance in mild cognitive impairment and Alzheimer's disease. Journal of clinical and experimental neuropsychology, 34(2), 195-208.
  • Yuan, Z., Yao, X., Bu, X. 2022. Classification of Alzheimer’s Disease Using Conventional Machine Learning Methods with Cortical and Genetic Characteristics. In 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), 21-23 January, Shenyang, 303-306.

SEÇİLMİŞ ÖZELLİKLER ÜZERİNDEN ALZHEİMER HASTALIĞININ YSA TABANLI ERKEN TEŞHİSİ

Year 2023, Volume: 11 Issue: 4, 1508 - 1516, 30.12.2023

Seyit Gazi Yıldız Kazım Yıldız

https://doi.org/10.21923/jesd.1296283

Abstract

Alzheimer Hastalığı(AH), bilişsel bozukluk olarak da adlandırılan bir tür demans hastalığıdır. Hastalığa karşı önlem alınmadığı durumlarda kişinin yaşam kalitesinde düşüşlere sebep olurken çok ciddi sonuçlarla neticelenebilir. Hastalıkla birlikte kişide düşünme ve hafıza yetisini kullanma fonksiyonlarında azalma görülebilir. Nörolojik sonuçlarla karşımıza çıkabileceği gibi, ileri durumlarda ölümle sonuçlanabilir. Tedavinin tam anlamıyla mümkün olmaması, AH için erken teşhis ve müdahalenin yerini önemli kılıyor. Çalışmada yapılan araştırmalar sonucunda, AH çerçevesinde birçok çalışma ve bilimsel içeriğin olduğu görülmüştür. Hastalığa özgü bazı değerlerin ve demografik özelliklerin yer aldığı açık kaynak olarak paylaşılmış veri seti kullanılarak, hastalığın erken teşhisine yönelik bir yöntem değerlendirilmiştir. Makine öğrenmesi yöntemlerinden Yapay Sinir Ağları (YSA) modeli kullanılarak, hastalığın erken tespiti yönünde önlemlerin zamanında alınmasına yönelik yapılacak diğer çalışmalara yararlı olması hedeflenmiştir. Demans ve demans olmayan birey şeklinde sınıflandırması yapılan Kök Ortalama Kare Hatası (KOKH) değeri 0.2302, Ortalama Mutlak Hata (OMH) değeri 0.1899 ve %98.5 doğruluk oranı elde edilmiştir.

Keywords

Alzheimer Hastalığı YSA Yapay Sinir Ağları Makine Öğrenmesi Hafif Bilişsel Bozukluk.

References

  • Acharya, S. 2021. What are RMSE and MAE?. https://towardsdatascience.com/what-are-rmse-and-mae-e405ce230383 (Access Date: 22.08.2023).
  • Aljović, A., Badnjević, A., Gurbeta, L. 2016. Artificial neural networks in the discrimination of Alzheimer's disease using biomarkers data. In 2016 5th Mediterranean Conference on Embedded Computing (MECO), 12-16 June, Bar, 286-289.
  • Buyrukoğlu, S. 2021. Early Detection of Alzheimer’s Disease Using Data Mining: Comparison of Ensemble Feature Selection Approaches. Konya Mühendislik Bilimleri Dergisi, 9(1), 50-61.
  • Chai, T., Draxler, R. R. 2014. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247-1250.
  • Delikanlı Akbay, G. 2019. Alzheimer Hastalığında B12 Vitamini Eksikliği. Cumhuriyet Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi, 4(3), 22-28.
  • Dinçer, B. 2018. Alzheimer Features. https://www.kaggle.com/datasets/brsdincer/alzheimer-features (Access Date: 01.12.2022).
  • Evyapan Akkuş, D., Güler, A. 2016. Ege Agrafi Test Bataryası'nın Hafif Bilişsel Bozukluk ve Alzheimer Hastalığı Olgularındaki Yazma Bozukluklarını Belirlemedeki Yeri. Türk Psikiyatri Dergisi, 27(3), 185-194.
  • Falahati, F., Westman, E., Simmons, A. 2014. Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging. Journal of Alzheimer's Disease, 41(3), 685-708.
  • Gerontogianni, L. 2022. Dates-Fruit-classification---PyTorch. https://github.com/Lina-Gerontogianni/Dates-Fruit-classification---PyTorch (Access Date: 01.12.2022).
  • Işık, İ. 2022. Classification of Alzheimer Disease with Molecular Communication Systems using LSTM. International Journal of Computational and Experimental Science and Engineering, 8(2), 25-31.
  • Karabay, G. S., Çavaş, M. 2022. Derin öğrenme Yöntemiyle Alzheimer Hastalığının Tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(2), 879-887
  • Kızrak, A. 2018. Şu Kara Kutuyu Açalım: Yapay Sinir Ağları. https://ayyucekizrak.medium.com/%C5%9Fu-kara-kutuyu-a%C3%A7alim-yapay-sinir-a%C4%9Flar%C4%B1-7b65c6a5264a (Access Date: 12.12.2022).
  • Klumpp, P., Fritsch, J., Nöth, E. 2018. ANN-based Alzheimer's disease classification from bag of words. In Speech Communication; 13th ITG-Symposium, 10-12 October, Oldenburg, 1-4.
  • Kour, H., Manhas, J., & Sharma, V. 2019. Evaluation of adaptive neuro-fuzzy inference system with artificial neural network and fuzzy logic in diagnosis of Alzheimer disease. In 2019 6th International conference on computing for sustainable global development (INDIACom), 13-15 March, New Delhi, 1041-1046.
  • Köseoğlu, B. 2021. Model Performansını Değerlendirmek: Regresyon. https://medium.com/yaz%C4%B1l%C4%B1m-ve-bili%C5%9Fim-kul%C3%BCb%C3%BC/model-performans%C4%B1n%C4%B1-de%C4%9Ferlendirmek-regresyon-48b4afec8664 (Access Date: 23.08.2023).
  • Mahajan, S., Bangar, G., Kulkarni, N. 2020. Machine Learning Algorithms for Classification of Various Stages of Alzheimer's Disease: A Review. Machine Learning, 7(08), 817-824.
  • Nancy Noella, R. S., Priyadarshini, J. 2020. Diagnosis of Alzheimer’s and Parkinson’s disease using artificial neural network. Int J Sci Technol Res, 9(3), 3659-3664.
  • Neelaveni, J., Devasana, M. S. G. 2020. Alzheimer Disease Prediction using Machine Learning Algorithms. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 6-7 March, Coimbatore, 101-104.
  • Özkaya, A., Cebeci, U. 2022. A Model Suggestion For Alzheimer’s Disease Diagnosis By Using Deep Learning. Avrupa Bilim ve Teknoloji Dergisi, (37), 123-130.
  • Salunkhe, S. Y., Chavan, M. S. 2022. Prediction of Alzheimer's disease using Machine Learning Algorithm. In 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), 26-28 January, Chiang Rai, 400-405.
  • Sertkaya, M. E., Ergen, B. 2022. Alzheimer Hastalığının Erken Teşhisinin Çoklu Değişken Kullanarak Tespiti. Avrupa Bilim ve Teknoloji Dergisi, (35), 306-314.
  • Sun, D., Peng, H., & Wu, Z. 2022. Establishment and analysis of a combined diagnostic model of alzheimer's disease with random forest and artificial neural network. Frontiers in Aging Neuroscience, 14, 921906.
  • Tufail, A. B., Abidi, A., Siddiqui, A. M., Younis, M. S. 2012. Automatic Classification of Initial Categories of Alzheimer's Disease from Structural MRI Phase Images: A Comparison of PSVM, KNN and ANN Methods. International Journal of Biomedical and Biological Engineering, 6(12), 713-717.
  • Quintana, M., Guàrdia, J., Sánchez-Benavides, G., Aguilar, M., Molinuevo, J. L., Robles, A., Barquero, M. A., Antúnez, C., Martínez-Parra, C., Frank-García, A., Fernández, M., Blesa, R., Peña-Casanova, J., Neuronorma Study Team 2012. Using artificial neural networks in clinical neuropsychology: High performance in mild cognitive impairment and Alzheimer's disease. Journal of clinical and experimental neuropsychology, 34(2), 195-208.
  • Yuan, Z., Yao, X., Bu, X. 2022. Classification of Alzheimer’s Disease Using Conventional Machine Learning Methods with Cortical and Genetic Characteristics. In 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), 21-23 January, Shenyang, 303-306.
There are 25 citations in total.

Details

Primary Language English
Subjects Computer Software, Engineering
Journal Section Research Articles
Authors

Seyit Gazi Yıldız 0000-0002-2708-2486

Kazım Yıldız 0000-0001-6999-1410

Publication Date December 30, 2023
Submission Date May 14, 2023
Acceptance Date November 2, 2023
Published in Issue Year 2023 Volume: 11 Issue: 4

Cite

APA Yıldız, S. G., & Yıldız, K. (2023). ANN BASED EARLY DETECTION OF ALZHEIMER DISEASE ON SELECTED FEATURES. Mühendislik Bilimleri Ve Tasarım Dergisi, 11(4), 1508-1516. https://doi.org/10.21923/jesd.1296283
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