An application that allows users to predict the risk of a patient having dementia based from their MRI Scan and other medical data
-
Three types of Ensemble methods were performed to model the data .i.e Bagging, Stacking and Boosting.
- Bagging using Random Forest
- Stacking using Decision Trees, Naive Bayes and K-Nearest Neighbors
- Boosting using XGBoost
-
Performed various Data Preprocessing techniques such as missing data imputation and removal of multicolinear features to clean and make the data ready for model building
-
Tuned hyperparameters of the model to achieve best performance.
-
Boosting had an accuracy of 86.76%, f1-score of 83.64% and recall of 85.19%
-
Model was deployed on a web application built using Django available at Dementia Predictor
Accuracy, F1-Score and Recall were the metrics used to evaluate the performance of the model
| Method | Accuracy (%) | F1-Score (%) | Recall (%) |
|---|---|---|---|
| Bagging | 85.29 | 82.14 | 85.19 |
| Stacking | 85.29 | 80.77 | 77.78 |
| Boosting | 86.76 | 83.64 | 85.19 |
Confusion Matrix
| 0 | 1 | |
|---|---|---|
| 0 | TN | FP |
| 1 | FN | TP |
| Variable | Data Object | Data type |
|---|---|---|
| MR Delay | The number of days between visits by a patient. | Integer |
| Gender | Gender of a patient (M or F) | Object |
| Hand | Patient’s dominant hand | Object |
| Age | Patient's age at the time of data collection | Integer |
| EDUC | Years of Education | Integer |
| SES | Socioeconomic status is classified into categories from 1 (highest status) to 5 (lowest status) | Integer |
| MMSE | Mini-mental State Examination score (range is from 0-worst to 30-best) | Integer |
| eTIV | Estimated total intracranial volume (cm3) | Integer |
| nWBV | Normalized whole brain volume | Float |
The final model with the best score was deployed on a web application built with Django with the frontend built with HTML & CSS with Boostrap 4 as the CSS Framework.
