This is my master's thesis where I delve in depth in the psycho and bio markers of Dementia, checking whether different types of Dementia can be diagnosed using Machine Learning. Then on top of these, I add some explainability using the Shapley method for Machine Learning. This, in principle, is a good starting ground for standardizing the Dementia Reporting for different patients, and can later become a valuable tool for clinicians.
- Thesis
- Thesis, Chapter 2 - Theory
- Thesis, Chapter 3 - Methodology
- Thesis, Chapter 4 - Results
- Thesis, Appendix (mainly on preprocessing)
- Notes
Note: This is previous to last version, and for some reason I dont have the last version. So, some references are missing. Keep your history folks.
Note: the following are not updated
| Subprocess | Possible options |
|---|---|
| Imbalance | |
| Over-sampling | No over-sampler, RandomOverSampler(), SMOTENC() |
| Under-sampling | No under-sampler, CustomHandler(), RandomUnderSampler(), NearMiss() |
| categorical | |
| Missing data | No imputation, SimpleImputer(*) |
| Transforming data | No encoding, OneHotEncoding() |
| Feature selection | No selection, SelectPercentile(Chi2), SelectFromModel(*) |
| numerical | |
| Missing data | No imputation, SimpleImputer(*), KNNImputer(), IterativeImputer(*) |
| Transforming data | No scaling, StandardScaler(), RobustScaler() |
| Feature selection | No selection, VarianceThreshold, SelectPercentile(Pearson) , SelectFromModel(*) |
| method | |
| Classifier | RandomForest(), XGBoost(), LightGBM(), HistGradientBoostingClassifier() |
| Cross-Validation | GridSearchCV(scorer='f1_micro', cv=StratifiedGroupKFold) |
Table: Transformers and Estimators used in the pipelines.
| Possible Pipeline | Sample selected Pipeline for ADNI |
|---|---|
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Note: In case you are using RandomForest, need to use a specific combination of preprocessing steps. To be updated.
For each class, the importance of the features are averaged, and then shared with the docs.
| Main predictors for big classes | Main predictors for smaller classes |
|---|---|
| (a) Healthy Controls | (b) Fronto-Temporal Lobe Degeneration |
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| (c) Alzheimer's Disease | (d) Dementia with Movement Disorders |
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The following are the importance of predictors for a single case. As you can see, its different from the predictors mentioned above, as the ones above are averaged among all the cases for a single class.
| Single case | Single case |
|---|---|
| (a) Healthy Control | (b) Fronto-Temporal Lobe Degeneration |
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| (c) Alzheimer's Disease | (d) Dementia with Movement Disorders |
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