●

●

●

Build the classification model

1. Select features 2. Split dataset 3. Build models 4. Assess models

- Remove highly correlated features (>0.75)

- Features reduced from 436 to 208

- 3 feature subsets● LVQ #20● RFE #15● Boruta #11

- Model Set: first 80%● Train 70%● Test 30 %

- Validation: last 20% data

- Linear methods: Linear Discriminant Analysis and Logistic Regression.

- Non-Linear methods: Neural Network, SVM, kNN

- Trees and Rules: CART

- Ensembles of Trees: Bagging CART, Random Forest and Stochastic Gradient Boosting

- Features selected using RFE gave the best results with the minimum error rate and the highest precision

- Bagging CART selected based on Cohen’s Kappa

(Kursa and Rudnicki 2010), (Guyon and Elisseeff 203)

(Holte 1993) (Lee, Lessler, and Stuart 2010), (Cutler and Zhao 2001), (Mohanbir 1996), (Kohavi 1995)

(Wilson 1927), (Cohen 1960)

Feat

ures

BACKUP

BACKUP

BACKUP

●

●

●

BACKUP

●

●

●

LVQ RFE Boruta

Accuracy 92% 94% 92%

Sensitivity 0% 15% 12%

Precision 0% 67% 33%

*Wilson score interval

BACKUP

LVQ RFE Boruta All

Testing Validation Testing Validation Testing Validation Testing Validation

Accuracy 92% 95% 94% 88% 92% 91% 91% 92%

Sensitivity 0% 6% 15% 0% 12% 0% 0% 6%

Precision 0% 50% 67% 0% 33% 0% 0% 10%

Dataset:Testing 375

Validation 314Prevalence 7%

Gradient Boosting

CART

Big Data Random Forest model

Deep neural

network

Accuracy 77% 85% 84%

Sensitivity 60% 48% 29%

Precision 17% 21% 15%

BACKUP

1.

2.