Araştırma Makalesi
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Yıl 2024, Cilt: 12 Sayı: 1, 214 - 223, 25.03.2024

Hakan Öcal

https://doi.org/10.29109/gujsc.1386416

Öz

Kaynakça

  • [1] Armstrong MJ, Litvan I, Lang AE, Bak TH, Bhatia KP, Borroni B, Boxer AL, Dickson DW, Grossman M, Hallett M, Josephs KA, Kertesz A, Lee SE, Miller BL, Reich SG, Riley DE, Tolosa E, Tröster AI, Vidailhet M, Weiner WJ. Criteria for the diagnosis of corticobasal degeneration. Neurology. 2013; 80 (5), 496–503.
  • [2] Alberdi A, Weakley A, Schmitter-Edgecombe M, Cook DJ, Aztiria A, Basarab A, Barrenechea M. Smart home-based prediction of multidomain symptoms related to Alzheimer’s disease, IEEE J. Biomed. Health Inform. 2018; 22:1720–31.
  • [3] Precup RE, Teban TA, Albu A., Borlea AB, Zamfirache IA, Petriu EM. Evolving fuzzy models for prosthetic hand myoelectric-based control. IEEE Trans. Instrum. Meas. 2020; 69: 4625–4636.
  • [4] Impedovo D, Pirlo G, Vessio G. Dynamic handwriting analysis for supporting earlier Parkinson’s disease diagnosis. Information. 2018; 9(10), 247.
  • [5] Myszczynska MA, Ojamies PN, Lacoste AMB, Neil D, Saffari A, Mead R, Hautbergue GM, Holbrook JD, Ferraiuolo L. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat. Rev. Neurol. 2020; 16: 440–456.
  • [6] Albu A, Precup R, Teban T. Results and challenges of artificial neural networks used for decision making and control in medical applications. Facta Univ. Ser.: Mech. Eng. 2019; 17(3): 285-308.
  • [7] Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT. Machine learning techniques for the diagnosis of Alzheimer’s disease: A review. ACM Trans. Multimedia Comput. Commun. Appl. 2020; 16(1): 1-35.
  • [8] Pereira CR, Pereira DR, Weber SA, Hook C, de Albuquerque VHC, Papa J. A survey on computer-assisted Parkinson’s disease diagnosis, Artif. Intell. Med. 2019; 95: 48–63.
  • [9] Ghaderyan P, Abbasi A, Ebrahimi A, Time-varying singular value decomposition analysis of electrodermal activity: A novel method of cognitive load estimation, Measurement, 2018; 126: 102-109.
  • [10] Jain N, Virmani D, Abraham A. Proficient 3-class classification model for confident overlap value-based fuzzified aquatic information extracted tsunami prediction. Intell. Decis. Technol. 2019; 13: 295–303.
  • [11] Borlea ID, Precup RE, Borlea AB, Iercan D. A unified form of fuzzy C-means an K-means algorithms and its partitional implementation. Knowl.-Based Syst. 2021; 214: 106731.
  • [12] Tonkal Ö, Polat H. Traffic Classification and Comparative Analysis with Machine Learning Algorithms in Software Defined Networks. Gazi University Journal of Science Part C: Design and Technology. 2021; 9(1): 71-83.
  • [13] Drotár P, Mekyska J, Rektorová I, Masarová L, Smékal Z, Faundez-Zanuy M. Evaluation of handwriting kinematics and pressure for differential diagnosis of parkinson’s disease. Artif. Intell. Med. 2016; 67: 39–46.
  • [14] Loconsole C, Cascarano GD, Brunetti A, Trotta GF, Losavio G, Bevilacqua V, Di Sciascio E. A model-free technique based on computer vision and sEMG for classification in Parkinson’s disease by using computer-assisted handwriting analysis. Pattern Recognition Letters. (2019); 121: 28-36.
  • [15] Moetesum M, Siddiqi I, Vincent N, Cloppet F. Assessing visual attributes of handwriting for prediction of neurological disorders—A case study on Parkinson’s disease. Pattern Recognition Letters. 2018; 121: 19-27.
  • [16] Slavin MJ, Phillips JG, Bradshaw JL, Hall KA, Presnell I. Consistency of handwriting movements in dementia of the Alzheimer’s type: a comparison with Huntington’s and Parkinson’s diseases. J. Int. Neuropsychol. 1999; 5(1): 20–25.
  • [17] Teulings HL, Stelmach GE. Control of stroke size, peak acceleration, and stroke duration in Parkinsonian handwriting. Hum. Mov. Sci. 1991; 10 (2): 315–334.
  • [18] Thomas M, Lenka A, Kumar Pal P. Handwriting Analysis in Parkinson’s Disease: Current Status and Future Directions. Movement Disorders Clinical Practice. 2017; 4(6): 806-818.
  • [19] Walton J, Handwriting changes due to aging and Parkinson’s syndrome. Forensic Sci. Int. 1997; 88(3): 197-214.
  • [20] Cilia ND, De Gregorio G, De Stefano C, Fontanella F, Marcelli A, Parziale A, Diagnosing Alzheimer’s disease from on-line handwriting: A novel dataset and performance benchmarking. Engineering Applications of Artificial Intelligence. 2022; 111: 0952-1976.
  • [21] Chai J, Wu R, Li A, Xue C, Qiang Y, Zhao J, Zhao Q, Yang Q. Classification of mild cognitive impairment based on handwriting dynamics and qEEG. Comput Biol Med. 2023; 152:106418.
  • [22] El-Yacoubi MA, Garcia-Salicetti S, Kahindo C, Rigaud AS, Cristancho-Lacroix V. From aging to early-stage Alzheimer’s: Uncovering handwriting multimodal behaviors by semi-supervised learning and sequential representation learning, Pattern Recognition. 2019; 86: 112-133.
  • [23] Kahindo C, El-Yacoubi MA, Garcia-Salicetti S, Cristancho-Lacroix V, Kerhervé H, Rigaud AS. Semi-global Parameterization of Online Handwriting Features for Characterizing Early-Stage Alzheimer and Mild Cognitive Impairment. IRBM. 2018; 39(6): 421-429.
  • [24] Sarin K, Bardamova M, Svetlakov M, Koryshev N, Ostapenko R, Hodashinskaya A, Hodashinsky I. A three-stage fuzzy classifier method for Parkinson’s disease diagnosis using dynamic handwriting analysis. Decision Analytics Journal. 2023; 8: 100274.

A Novel Approach to Detection of Alzheimer’s Disease from Handwriting: Triple Ensemble Learning Model

Yıl 2024, Cilt: 12 Sayı: 1, 214 - 223, 25.03.2024

Hakan Öcal

https://doi.org/10.29109/gujsc.1386416

Öz

The irreversible degeneration of nerve cells in the body dramatically affects the motor skills and cognitive abilities used effectively in daily life. There is no known cure for neurodegenerative diseases such as Alzheimer’s. However, in the early diagnosis of such diseases, the progression of the disease can be slowed down with specific rehabilitation techniques and medications. Therefore, early diagnosis of the disease is essential in slowing down the disease and improving patients’ quality of life. Neurodegenerative diseases also affect patients’ ability to use fine motor skills. Losing fine motor skills causes patients’ writing skills to deteriorate gradually. Information about Alzheimer’s disease can be obtained based on the deterioration in the patient’s writing skills. However, manual detection of Alzheimer’s disease (AD) from handwriting is a time-consuming and challenging task that varies from physician to physician. Machine learning-based classifiers are extremely popularly used with high-performance scores to solve the challenging manual detection of AD. In this study, Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) machine learning classification algorithms were combined with a Voting Classifier and trained and tested on the publicly available DARWIN (Diagnosis Alzheimer’s With haNdwriting) dataset. As a result of the experimental studies, the proposed Ensemble methodology achieved 97.14% Acc, 95% Prec, 100% Recall, 90.25% Spec, and 97.44% F1-score (Dice) performance values. Studies have shown that the proposed work is exceptionally robust.

Anahtar Kelimeler

Neurodegenerative disease Alzheimer’s disease prediction ensemble machine learning model classification handwriting data

Kaynakça

  • [1] Armstrong MJ, Litvan I, Lang AE, Bak TH, Bhatia KP, Borroni B, Boxer AL, Dickson DW, Grossman M, Hallett M, Josephs KA, Kertesz A, Lee SE, Miller BL, Reich SG, Riley DE, Tolosa E, Tröster AI, Vidailhet M, Weiner WJ. Criteria for the diagnosis of corticobasal degeneration. Neurology. 2013; 80 (5), 496–503.
  • [2] Alberdi A, Weakley A, Schmitter-Edgecombe M, Cook DJ, Aztiria A, Basarab A, Barrenechea M. Smart home-based prediction of multidomain symptoms related to Alzheimer’s disease, IEEE J. Biomed. Health Inform. 2018; 22:1720–31.
  • [3] Precup RE, Teban TA, Albu A., Borlea AB, Zamfirache IA, Petriu EM. Evolving fuzzy models for prosthetic hand myoelectric-based control. IEEE Trans. Instrum. Meas. 2020; 69: 4625–4636.
  • [4] Impedovo D, Pirlo G, Vessio G. Dynamic handwriting analysis for supporting earlier Parkinson’s disease diagnosis. Information. 2018; 9(10), 247.
  • [5] Myszczynska MA, Ojamies PN, Lacoste AMB, Neil D, Saffari A, Mead R, Hautbergue GM, Holbrook JD, Ferraiuolo L. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat. Rev. Neurol. 2020; 16: 440–456.
  • [6] Albu A, Precup R, Teban T. Results and challenges of artificial neural networks used for decision making and control in medical applications. Facta Univ. Ser.: Mech. Eng. 2019; 17(3): 285-308.
  • [7] Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT. Machine learning techniques for the diagnosis of Alzheimer’s disease: A review. ACM Trans. Multimedia Comput. Commun. Appl. 2020; 16(1): 1-35.
  • [8] Pereira CR, Pereira DR, Weber SA, Hook C, de Albuquerque VHC, Papa J. A survey on computer-assisted Parkinson’s disease diagnosis, Artif. Intell. Med. 2019; 95: 48–63.
  • [9] Ghaderyan P, Abbasi A, Ebrahimi A, Time-varying singular value decomposition analysis of electrodermal activity: A novel method of cognitive load estimation, Measurement, 2018; 126: 102-109.
  • [10] Jain N, Virmani D, Abraham A. Proficient 3-class classification model for confident overlap value-based fuzzified aquatic information extracted tsunami prediction. Intell. Decis. Technol. 2019; 13: 295–303.
  • [11] Borlea ID, Precup RE, Borlea AB, Iercan D. A unified form of fuzzy C-means an K-means algorithms and its partitional implementation. Knowl.-Based Syst. 2021; 214: 106731.
  • [12] Tonkal Ö, Polat H. Traffic Classification and Comparative Analysis with Machine Learning Algorithms in Software Defined Networks. Gazi University Journal of Science Part C: Design and Technology. 2021; 9(1): 71-83.
  • [13] Drotár P, Mekyska J, Rektorová I, Masarová L, Smékal Z, Faundez-Zanuy M. Evaluation of handwriting kinematics and pressure for differential diagnosis of parkinson’s disease. Artif. Intell. Med. 2016; 67: 39–46.
  • [14] Loconsole C, Cascarano GD, Brunetti A, Trotta GF, Losavio G, Bevilacqua V, Di Sciascio E. A model-free technique based on computer vision and sEMG for classification in Parkinson’s disease by using computer-assisted handwriting analysis. Pattern Recognition Letters. (2019); 121: 28-36.
  • [15] Moetesum M, Siddiqi I, Vincent N, Cloppet F. Assessing visual attributes of handwriting for prediction of neurological disorders—A case study on Parkinson’s disease. Pattern Recognition Letters. 2018; 121: 19-27.
  • [16] Slavin MJ, Phillips JG, Bradshaw JL, Hall KA, Presnell I. Consistency of handwriting movements in dementia of the Alzheimer’s type: a comparison with Huntington’s and Parkinson’s diseases. J. Int. Neuropsychol. 1999; 5(1): 20–25.
  • [17] Teulings HL, Stelmach GE. Control of stroke size, peak acceleration, and stroke duration in Parkinsonian handwriting. Hum. Mov. Sci. 1991; 10 (2): 315–334.
  • [18] Thomas M, Lenka A, Kumar Pal P. Handwriting Analysis in Parkinson’s Disease: Current Status and Future Directions. Movement Disorders Clinical Practice. 2017; 4(6): 806-818.
  • [19] Walton J, Handwriting changes due to aging and Parkinson’s syndrome. Forensic Sci. Int. 1997; 88(3): 197-214.
  • [20] Cilia ND, De Gregorio G, De Stefano C, Fontanella F, Marcelli A, Parziale A, Diagnosing Alzheimer’s disease from on-line handwriting: A novel dataset and performance benchmarking. Engineering Applications of Artificial Intelligence. 2022; 111: 0952-1976.
  • [21] Chai J, Wu R, Li A, Xue C, Qiang Y, Zhao J, Zhao Q, Yang Q. Classification of mild cognitive impairment based on handwriting dynamics and qEEG. Comput Biol Med. 2023; 152:106418.
  • [22] El-Yacoubi MA, Garcia-Salicetti S, Kahindo C, Rigaud AS, Cristancho-Lacroix V. From aging to early-stage Alzheimer’s: Uncovering handwriting multimodal behaviors by semi-supervised learning and sequential representation learning, Pattern Recognition. 2019; 86: 112-133.
  • [23] Kahindo C, El-Yacoubi MA, Garcia-Salicetti S, Cristancho-Lacroix V, Kerhervé H, Rigaud AS. Semi-global Parameterization of Online Handwriting Features for Characterizing Early-Stage Alzheimer and Mild Cognitive Impairment. IRBM. 2018; 39(6): 421-429.
  • [24] Sarin K, Bardamova M, Svetlakov M, Koryshev N, Ostapenko R, Hodashinskaya A, Hodashinsky I. A three-stage fuzzy classifier method for Parkinson’s disease diagnosis using dynamic handwriting analysis. Decision Analytics Journal. 2023; 8: 100274.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgi Sistemleri Eğitimi, Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları
Bölüm Tasarım ve Teknoloji
Yazarlar

Hakan Öcal 0000-0002-8061-8059

Erken Görünüm Tarihi 7 Mart 2024
Yayımlanma Tarihi 25 Mart 2024
Gönderilme Tarihi 5 Kasım 2023
Kabul Tarihi 13 Şubat 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 12 Sayı: 1

Kaynak Göster

APA Öcal, H. (2024). A Novel Approach to Detection of Alzheimer’s Disease from Handwriting: Triple Ensemble Learning Model. Gazi University Journal of Science Part C: Design and Technology, 12(1), 214-223. https://doi.org/10.29109/gujsc.1386416
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