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

Hakan Öcal

Makale detayları

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

Yazarlar:

Hakan Öcal

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri Eğitimi | Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 1, pp.214-223

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313831

pISSN:

2147-9526

Anahtar kelimeler:

Özet:

 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 Alzheimers. 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 Alzheimers disease can be obtained based on the deterioration in the patients writing skills. However, manual detection of Alzheimers 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 Alzheimers 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.

 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 Alzheimers. 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 Alzheimers disease can be obtained based on the deterioration in the patients writing skills. However, manual detection of Alzheimers 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 Alzheimers 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.

 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 Alzheimers. 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 Alzheimers disease can be obtained based on the deterioration in the patients writing skills. However, manual detection of Alzheimers 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 Alzheimers 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.

 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 Alzheimers. 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 Alzheimers disease can be obtained based on the deterioration in the patients writing skills. However, manual detection of Alzheimers 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 Alzheimers 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.

Makale detayları

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

Yazarlar:

Hakan Öcal

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri Eğitimi | Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 1, pp.214-223

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313831

pISSN:

2147-9526

Neurodegenerative disease

Alzheimer’s disease prediction

ensemble machine learning model

classification

handwriting data

Sayı

Makale detayları

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

Yazarlar: Hakan Öcal

Yayıncı: Gazi University

Ana Başlıklar: Bilgi Sistemleri Eğitimi | Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları

Dergi: Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 1, pp.214-223

Belge Tipi: Makale

Yayın Tarihi: 3/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001313831

pISSN: 2147-9526

Neurodegenerative disease

Alzheimer’s disease prediction

ensemble machine learning model

classification

handwriting data

Sayı

Yazarlar:

Hakan Öcal

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri Eğitimi | Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 1, pp.214-223

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313831

pISSN:

2147-9526