Cognitive Handwriting Insights for Alzheimer’s Diagnosis: A Hybrid Framework
Abstract
:1. Introduction
- Introduction of a Novel Hybrid Model: The research introduces a novel predictive model designed to identify Alzheimer’s disease (AD) at an early stage (in stage 2 or 3, as mentioned in Table 1). This hybrid model seamlessly integrates both unsupervised and supervised approaches, delivering a harmonious blend of rapidity, streamlined design, and enhanced accuracy that surpasses current state-of-the-art methodologies.
- Integration of Fuzzy Logic with Machine Learning: A creative strategy merges “Fuzzy Logic” with “Machine Learning”, enhancing the efficacy of conventional classifiers and introducing a novel dimension into medical diagnosis.
- Lightweight Model for Medium Datasets: Introducing a lightweight model tailored for training on medium-sized datasets, ensuring practicality and efficiency, especially in settings with limited data accessibility.
- Comprehensive Set of Baselines: Incorporating a diverse set of baseline classifiers for benchmarking, enabling a thorough assessment of the proposed model against existing methods of AD detection.
- Use of Clustering Validity Index: Multiple clustering validity indices are employed to determine the optimal number of clusters, enhancing data pattern comprehension by considering membership values.
- Extensive Hyperparameter Optimization: Automated hyperparameter optimization ensures the identification of the most optimal model, resulting in enhanced accuracy in AD detection.
Related Work
2. Materials and Methods
2.1. Dataset Construction
2.1.1. Handwriting Data Collection Process
2.1.2. Feature Extraction from Handwriting Data
2.1.3. Time-Based Features (How Long the Writing Takes)
- Total Time (TT): Measures the total duration required to complete a task.
- Air Time (AT) & Paper Time (PT): Differentiates between the time spent writing on the paper and the time when the pen is lifted. A longer air time may indicate hesitation or difficulty in initiating movement.
2.1.4. Speed and Movement Features (How the Hand Moves During Writing)
- Mean Speed on-paper (MSP) & Mean Speed in-air (MSA): Measure the average speed of writing and movement in the air, which may be slower in AD patients due to motor impairments.
- Mean Acceleration (MAP, MAA): Assesses how quickly writing speed changes, indicating the ability to control movement.
- Mean Jerk (MJP, MJA): Measures how smoothly or abruptly the hand moves, providing insights into motor control and stability.
2.1.5. Pressure-Based Features (How Hard the Pen Is Pressed on the Paper)
- Pressure Mean (PM) & Pressure Variance (PV): Assess the force applied while writing. Inconsistencies in pressure may suggest declining motor function or hand tremors.
2.1.6. Tremor and Stability Features (How Steady the Writing Is)
- Generalized Mean Relative Tremor (GMRT, GMRTP, GMRTA): Identifies hand tremors, which are common in neurological disorders.
2.1.7. Spatial and Structural Features (How Writing Is Organized on the Page)
- Max X and Y Extensions (XE, YE): Measures the size of handwriting strokes. Changes in stroke size can indicate cognitive and motor decline.
- Dispersion Index (DI): Evaluates whether handwriting is spread evenly or scattered, which may reflect spatial planning difficulties.
2.1.8. Task-Specific Features (Additional Behavioral Indicators)
- Pen-down Number (PWN): Counts how many times the pen touches the paper, reflecting writing fluency and control.
2.2. Software Stack
- pandas and numpy: These libraries are fundamental for data manipulation, providing efficient data structures and array operations necessary for handling and processing datasets.
- sklearn.preprocessing.StandardScaler: Used for standardizing data, ensuring features are on the same scale, which is crucial for many machine learning algorithms to perform optimally.
- scikit-learn (sklearn): This library forms the backbone of the machine learning pipeline, offering a comprehensive suite of algorithms and tools for modeling, evaluation, and cross-validation. Specific classifiers utilized included
- DecisionTreeClassifier (DT)
- RandomForestClassifier (RF)
- ExtraTreesClassifier (ET)
- LogisticRegression (LR)
- LinearDiscriminantAnalysis (LDA)
- GaussianNB (GNB)
- XGBClassifier (XGB)
- DecisionTreeClassifier (DT)
- MLPClassifier (MLP)
- KNeighborsClassifier (KNN)
- matplotlib: Employed for data visualization, enabling the generation of insightful plots and graphs for data exploration, model evaluation, and comparison of results across different algorithms.
2.3. Development of the Proposed Model
2.3.1. Stage 1: Cognitive Feature Generation Using Unsupervised Learning
- is the fuzzy membership matrix, with representing the membership of data point to cluster j.
- is the matrix of cluster centers, with .
- is the fuzzifier parameter, controlling the degree of fuzziness.
- Initialize U and V.
- Repeat until convergence:
- (a)
- Update cluster centers :
- (b)
- Update the fuzzy membership matrix U:
- is the average distance from point i to other points within the same cluster.
- is the smallest average distance from point i to points in a different cluster.
- k is the number of clusters.
- and are clusters.
- is a measure of similarity between clusters and .
- is a measure of dissimilarity between clusters and .
- B is the between-cluster dispersion.
- W is the within-cluster dispersion.
- N is the total number of data points.
- k is the number of clusters.
- is the separation between clusters and .
- is the diameter of cluster .
2.3.2. Stage 2: Prognostic Model Development Using Supervised Learning
2.3.3. The Proposed Model with Optimal Setup
2.4. Algorithm
Algorithm 1 CogniGen computes “cognitive features” from “clinical features” |
Input: Dataset X, maximum number of clusters N, fuzziness parameter m, termination criterion , maximum number of iterations I. Output: Optimal number of clusters , Final Cluster Centers , Fuzzy Membership Matrix , Silhouette Scores array S, Davies–Bouldin Scores array , Calinski–Harabasz Scores array , Dunn Scores array D for to N.
|
Algorithm 2 Fuzzy C-Means Clustering |
Input: Dataset X, number of clusters C, fuzziness parameter m, termination criterion , maximum number of iterations. Output: Cluster centers , Membership matrix U.
|
Algorithm 3 Random Forest Algorithm |
|
2.5. Performance Evaluation Metrics
- True Positive (TP): The number of instances correctly predicted as positive.
- True Negative (TN): The number of instances correctly predicted as negative.
- False Positive (FP): The number of instances incorrectly predicted as positive.
- False Negative (FN): The number of instances incorrectly predicted as negative.
2.6. Training of the Proposed Model
3. Results
3.1. Results from Unsupervised Machine Learning
3.2. Results from Supervised Machine Learning
3.2.1. Applying Classifiers Without Stage 1 (FCM)
3.2.2. Applying Classifiers with Stage 1 (FCM)
3.3. Comparative Performance
4. Conclusions, Limitations, and Future Work
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Stage | Level of Impairments | Details |
---|---|---|
1 | No impairment | Healthy |
2 | Very mild cognitive decline | Healthy |
3 | Mild cognitive decline | Healthy |
4 | Moderate cognitive decline | Early-stage dementia |
5 | Moderately severe cognitive decline | Early mid-stage dementia |
6 | Severe cognitive decline | Late mid-stage dementia |
7 | Very severe cognitive decline | Late-stage dementia |
Stage | Steps | Purpose |
---|---|---|
Stage 1: | Cognitive clustering based on fuzzy similarity | Group data points based on fuzzy similarity to handle uncertainty in handwriting patterns. |
Preferred number of clusters identified by multiple CVI | Use multiple cluster validity indices (CVI) to determine the optimal number of clusters. | |
Decision-level fusion of multiple CVI scores | Combine multiple CVI scores to make a robust decision on the optimal cluster count. | |
Optimal cluster number from the fusion | Select the best cluster number that represents the data structure effectively. | |
Cluster membership values for each cluster extracted as features | Use cluster membership values as features for supervised classification. | |
Stage 2: | Cluster membership values used as features, along with class labels | Integrate clustering results into the machine learning model as additional features. |
Ten-fold cross-validation technique applied | Ensure model generalization and prevent overfitting by using cross-validation. | |
Model training performed | Train a supervised learning model using the extracted features. | |
Prediction and validation processes executed | Evaluate model performance and validate predictions for classification accuracy. |
Study | ML Model Used | Feature/Data Modality | Techniques Used | Accuracy |
---|---|---|---|---|
De Gregorio et al. [40] | RF, KNN, LDA, GNB, SVM | Task specific | Ensemble multiple task-specific classifiers built upon a single type of ML model | 91% |
Cilia et al. [41] | RF, LR, KNN, LDA, GNB, SVM, DT, MLP, LVQ | Task specific | Ensemble multiple task-specific classifiers built upon top-performing and different types of ML models | 94.28% |
Parziale et al. [42] | Negative Selection Algorithm, Isolation Forest, One-Class SVM | All features | One-class classifiers are utilized | 97% |
Subha et al. [43] | LR, KNN, SVM, DT, RF, AdaBoost with Swarm Intelligence | All features | Swarm intelligence-based feature selection employed with ML models | 90% |
Gattulli et al. [44] | RF, LR, KNN, LDA, SVM, BN, GNB, MP, LVQ | Task specific | Mixed tasks of different levels of complexity | 88% |
Onder et al. [45] | XGBoost, GradientBoost, AdaBoost | All features | Categorization methods are employed | 85% |
Hakan et al. [46] | Ensemble of LGBM, CatBoost, AdaBoost | All features | Hard ensemble of the employed models | 97% |
Mitra and Rehman [47] | Stacking | Selected top-k features | Classifier-specific top-k features are utilized for training the classifiers with LR as the meta-level | 97% |
Erdogmus et al. [48] | CNN | 1D features converted into 2D images | Pre-trained models were utilized for training the classifiers with the constructed 2D images | 90% |
Participant Class | Age (Mean ± SD) | # Male | # Female |
---|---|---|---|
Patients | 71.5 (9.5) | 44 | 46 |
Healthy People | 68.9 (12) | 39 | 51 |
Proposed Model | Hyper-Parameter |
---|---|
FCM + RF | Optimal cluster center = 2, |
{‘max_depth’: None, ‘min_samples_leaf’: 1, ‘min_samples_split’: 2, ‘n_estimators’: 50} |
Actual | Predicted | |
---|---|---|
Positive | Negative | |
Positive | TP | FN |
Negative | FP | TN |
Algorithm | Param_Grid | Best_Param | Accuracy | No of Trials |
---|---|---|---|---|
DT | ‘max_depth’: [None, 10, 20], ‘min_samples_split’: [2, 5, 10], ‘min_samples_leaf’: [1, 2, 4] | { ‘max_depth’: 20, ‘min_samples_leaf’: 4, ‘min_samples_split’: 2} | 85.71% | 135 |
RF | ‘n_estimators’: [50, 100, 150], ‘max_depth’: [None, 10, 20], ‘min_samples_split’: [2, 5, 10], ‘min_samples_leaf’: [1, 2, 4] | { ‘max_depth’: 20, ‘min_samples_leaf’: 4, ‘min_samples_split’: 5, ‘n_estimators’: 150} | 85.71% | 405 |
Extra Tree | ‘n_estimators’: [50, 100, 150], ‘max_depth’: [None, 10, 20], ‘min_samples_split’: [2, 5, 10], ‘min_samples_leaf’: [1, 2, 4] | { ‘max_depth’: 10, ‘min_samples_leaf’: 1, ‘min_samples_split’: 10, ‘n_estimators’: 150} | 88.57% | 405 |
LR | ‘penalty’: [‘l1’, ‘l2’], ‘C’: [0.001, 0.01, 0.1, 1, 10, 100] | { ‘C’: 10, ‘penalty’: ‘l2’ } | 88.57% | 60 |
LDA | ‘solver’: [‘svd’, ‘lsqr’, ‘eigen’], ‘shrinkage’: [None, ‘auto’], ‘n_components’: [None, 1, 2] | { ‘n_components’: None, ‘shrinkage’: ‘auto’, ‘solver’: ‘lsqr’} | 88.57% | 90 |
GNB | No tunable parameters | Not applicable | 85.71% | Not applicable |
XGB | ‘learning_rate’: [0.05, 0.1, 0.3], ‘max_depth’: [3, 6, 9], ‘min_child_weight’: [1, 3, 5], ‘gamma’: [0, 0.1, 0.2], ‘subsample’: [0.6, 0.8, 1.0], ‘colsample_bytree’: [0.6, 0.8, 1.0], ‘n_estimators’: [50, 100, 150] | { ‘colsample_bytree’: 0.8, ‘gamma’: 0, ‘learning_rate’: 0.1, ‘max_depth’: 6, ‘min_child_weight’: 1, ‘n_estimators’: 100, ‘subsample’: 1.0} | 88.57% | 10935 |
KNN | ‘n_neighbors’: [3, 5, 7], ‘weights’: [‘uniform’, ‘distance’], ‘metric’: [‘euclidean’, ‘manhattan’] | { ‘metric’: ‘manhattan’, ‘n_neighbors’: 3, ‘weights’: ‘uniform’} | 48.57% | 60 |
SVM | ‘C’: [0.1, 1, 10], ‘kernel’: [‘linear’, ‘rbf’], ‘gamma’: [‘scale’, ‘auto’] | { ‘C’: 1, ‘gamma’: ‘scale’, ‘kernel’: ‘rbf’ } | 91.43% | 60 |
MLP | ‘hidden_layer_sizes’: [(100,), (50, 100, 50)], ‘activation’: [‘relu’, ‘tanh’], ‘solver’: [‘adam’, ‘sgd’], ‘alpha’: [0.0001, 0.001], ‘learning_rate’: [‘constant’, ‘adaptive’] | { ‘activation’: ‘relu’, ‘alpha’: 0.001, ‘hidden_layer_sizes’: (100,), ‘learning_rate’: ‘constant’, ‘solver’: ‘sgd’} | 85.71% | 160 |
Model | Accuracy | Sensitivity | Specificity | Precision | MCC | Cohen’s Kappa | AUC ROC |
---|---|---|---|---|---|---|---|
DT | 0.7327 ± 0.1741 | 0.725 ± 0.2683 | 0.7306 ± 0.2041 | 0.7555 ± 0.1992 | 0.4748 ± 0.3496 | 0.4539 ± 0.3543 | 0.7278 ± 0.1768 |
RF | 0.8376 ± 0.1255 | 0.900 ± 0.1606 | 0.7986 ± 0.1351 | 0.8319 ± 0.0902 | 0.7157 ± 0.1821 | 0.6981 ± 0.1921 | 0.8493 ± 0.0959 |
ET | 0.8255 ± 0.1289 | 0.8875 ± 0.1649 | 0.8111 ± 0.1608 | 0.8398 ± 0.1209 | 0.7119 ± 0.2394 | 0.6983 ± 0.2473 | 0.8493 ± 0.1227 |
LR | 0.7908 ± 0.1450 | 0.8194 ± 0.1745 | 0.7667 ± 0.2073 | 0.8071 ± 0.1603 | 0.6024 ± 0.2854 | 0.5837 ± 0.2889 | 0.7931 ± 0.1440 |
LDA | 0.8144 ± 0.1232 | 0.8194 ± 0.1597 | 0.8097 ± 0.1924 | 0.8453 ± 0.1394 | 0.6449 ± 0.2441 | 0.6278 ± 0.2491 | 0.8146 ± 0.1245 |
GNB | 0.8376 ± 0.1118 | 0.8653 ± 0.1632 | 0.8125 ± 0.1341 | 0.8330 ± 0.1009 | 0.6876 ± 0.2170 | 0.6761 ± 0.2226 | 0.8389 ± 0.1107 |
XGB | 0.8611 ± 0.0885 | 0.9000 ± 0.1160 | 0.8208 ± 0.1713 | 0.8615 ± 0.1102 | 0.7371 ± 0.1735 | 0.7201 ± 0.1819 | 0.8604 ± 0.0924 |
KNN | 0.6526 ± 0.1351 | 0.3444 ± 0.2406 | 0.9764 ± 0.0473 | 0.8667 ± 0.3055 | 0.3800 ± 0.2878 | 0.3163 ± 0.2670 | 0.6604 ± 0.1331 |
SVM | 0.8141 ± 0.1462 | 0.8403 ± 0.1638 | 0.7847 ± 0.2345 | 0.8305 ± 0.1585 | 0.6397 ± 0.2974 | 0.6244 ± 0.2994 | 0.8125 ± 0.1499 |
MLP | 0.4824 ± 0.0844 | 0.8000 ± 0.4000 | 0.2000 ± 0.4000 | NaN | 0.0000 ± 0.0000 | 0.0000 ± 0.0000 | 0.5000 ± 0.0000 |
Algorithm | Param_Grid | Best_Param | Acc | No of Trials |
---|---|---|---|---|
FCM | Cluster center to 8 | Best CVI score | 7 | |
DT | ‘max_depth’: [None, 10, 20], ‘min_samples_split’: [2, 5, 10], ‘min_samples_leaf’: [1, 2, 4] | ‘max_depth’: None, ‘min_samples_leaf’: 1, ‘min_samples_split’: 2 | 135 | |
RF | ‘n_estimators’: [50, 100, 150], ‘max_depth’: [None, 10, 20], ‘min_samples_split’: [2, 5, 10], ‘min_samples_leaf’: [1, 2, 4] | ‘max_depth’: None, ‘min_samples_leaf’: 1, ‘min_samples_split’: 2, ‘n_estimators’: 50 | 405 | |
Extra Tree | ‘n_estimators’: [50, 100, 150], ‘min_samples_split’: [2, 5, 10], ‘min_samples_leaf’: [1, 2, 4] | ‘min_samples_leaf’: 1, ‘min_samples_split’: 2, ‘n_estimators’: 50 | 135 | |
LR | ‘C’: [0.001, 0.01, 0.1, 1, 10, 100], ‘penalty’: [‘l1’, ‘l2’], ‘solver’: [‘liblinear’] | ‘C’: 0.001, ‘penalty’: ‘l2’, ‘solver’: ‘liblinear’ | 60 | |
LDA | ‘solver’: [‘svd’, ‘lsqr’, ‘eigen’] | ‘solver’: ‘svd’ | 15 | |
GNB | No tunable parameter | Not applicable | NA | |
XGB | ‘learning_rate’: [0.05, 0.1, 0.3], ‘max_depth’: [3, 6, 9], ‘min_child_weight’: [1, 3, 5], ‘gamma’: [0, 0.1, 0.2], ‘subsample’: [0.6, 0.8, 1.0], ‘colsample_bytree’: [0.6, 0.8, 1.0], ‘n_estimators’: [50, 100, 150] | ‘colsample_bytree’: 0.6, ‘gamma’: 0, ‘learning_rate’: 0.05, ‘max_depth’: 3, ‘min_child_weight’: 1, ‘n_estimators’: 50, ‘subsample’: 0.8 | 10935 | |
KNN | ‘n_neighbors’: [3, 5, 7], ‘weights’: [‘uniform’, ‘distance’], ‘p’: [1, 2] | ‘n_neighbors’: 3, ‘p’: 1, ‘weights’: ‘uniform’ | 60 | |
SVM | ‘C’: [0.1, 1, 10], ‘gamma’: [‘scale’, ‘auto’], ‘kernel’: [‘linear’, ‘rbf’] | ‘C’: 0.1, ‘gamma’: ‘scale’, ‘kernel’: ‘rbf’ | 60 | |
MLP | ‘hidden_layer_sizes’: [(100,), (50, 50), (50, 50, 50)], ‘activation’: [‘relu’, ‘tanh’], ‘solver’: [‘adam’], ‘alpha’: [0.0001, 0.001], ‘learning_rate’: [‘constant’, ‘adaptive’] | ‘activation’: ‘relu’, ‘alpha’: 0.0001, ‘hidden_layer_sizes’: (100,), ‘learning_rate’: ‘adaptive’, ‘solver’: ‘adam’ | 120 |
Model | Accuracy | Sensitivity | Specificity | Precision | MCC | Cohen’s Kappa | AUC ROC |
---|---|---|---|---|---|---|---|
DT | 0.9944 ± 0.0167 | 1.0000 ± 0.0000 | 0.9889 ± 0.0333 | 0.9900 ± 0.0300 | 0.9894 ± 0.0317 | 0.9889 ± 0.0333 | 0.9944 ± 0.0167 |
RF | 0.9944 ± 0.0167 | 0.9889 ± 0.0333 | 1.0000 ± 0.0000 | 1.0000 ± 0.0000 | 0.9894 ± 0.0317 | 0.9889 ± 0.0333 | 0.9944 ± 0.0167 |
ET | 0.9944 ± 0.0167 | 1.0000 ± 0.0000 | 0.9889 ± 0.0333 | 0.9900 ± 0.0300 | 0.9894 ± 0.0317 | 0.9889 ± 0.0333 | 0.9944 ± 0.0167 |
LR | 0.4232 ± 0.0973 | 0.8000 ± 0.4000 | 0.2000 ± 0.4000 | NaN | 0.0000 ± 0.0000 | 0.0000 ± 0.0000 | 0.5000 ± 0.0000 |
LDA | 0.9716 ± 0.0460 | 0.9471 ± 0.0837 | 1.0000 ± 0.0000 | 1.0000 ± 0.0000 | 0.9473 ± 0.0847 | 0.9424 ± 0.0930 | 0.9735 ± 0.0419 |
GNB | 0.9775 ± 0.0372 | 1.0000 ± 0.0000 | 0.9562 ± 0.0735 | 0.9584 ± 0.0667 | 0.9572 ± 0.0693 | 0.9540 ± 0.0752 | 0.9781 ± 0.0368 |
XGB | 0.9889 ± 0.0333 | 0.9778 ± 0.0667 | 1.0000 ± 0.0000 | 1.0000 ± 0.0000 | 0.9798 ± 0.0607 | 0.9778 ± 0.0667 | 0.9889 ± 0.0333 |
KNN | 0.9944 ± 0.0167 | 1.0000 ± 0.0000 | 0.9889 ± 0.0333 | 0.9900 ± 0.0300 | 0.9894 ± 0.0317 | 0.9889 ± 0.0333 | 0.9944 ± 0.0167 |
SVM | 0.9771 ± 0.0375 | 0.9609 ± 0.0656 | 1.0000 ± 0.0000 | 1.0000 ± 0.0000 | 0.9570 ± 0.0692 | 0.9538 ± 0.0751 | 0.9805 ± 0.0328 |
MLP | 0.4644 ± 0.1187 | 0.8000 ± 0.4000 | 0.2000 ± 0.4000 | NaN | 0.0000 ± 0.0000 | 0.0000 ± 0.0000 | 0.5000 ± 0.0000 |
Model | Acc (%) | Sn (%) | Sp (%) | Precision (%) | AUC-ROC (%) |
---|---|---|---|---|---|
De Gregorio et al. [40] | 91 | 83 | 100 | - | - |
Cilia et al. [41] | 94.28 | 88.24 | 100 | - | - |
Parziale et al. [42] | 97.12 | 94.23 | 100 | - | - |
Subha et al. [43] | 90 | 92 | - | 88 | 90 |
Gattulli et al. [44] | 88 | 90 | 86 | - | - |
Onder et al. [45] | 85 | - | - | - | - |
Hakan et al. [46] | 97.14 | - | 90 | 95 | - |
Erdogmus et al. [48] | 90.4 | - | - | - | - |
Mitra and Rehman [47] | 97.14 | 95 | 100 | 100 | 94.21 |
Proposed Model | 99.44 | 100 | 100 | 100 | 99.44 |
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Ul Rehman, S.; Mitra, U. Cognitive Handwriting Insights for Alzheimer’s Diagnosis: A Hybrid Framework. Information 2025, 16, 249. https://doi.org/10.3390/info16030249
Ul Rehman S, Mitra U. Cognitive Handwriting Insights for Alzheimer’s Diagnosis: A Hybrid Framework. Information. 2025; 16(3):249. https://doi.org/10.3390/info16030249
Chicago/Turabian StyleUl Rehman, Shafiq, and Uddalak Mitra. 2025. "Cognitive Handwriting Insights for Alzheimer’s Diagnosis: A Hybrid Framework" Information 16, no. 3: 249. https://doi.org/10.3390/info16030249
APA StyleUl Rehman, S., & Mitra, U. (2025). Cognitive Handwriting Insights for Alzheimer’s Diagnosis: A Hybrid Framework. Information, 16(3), 249. https://doi.org/10.3390/info16030249