## 3 Results
### Feature Selection  
In the first experiment we analyzed the predictive power of the variables for classification using RFE with RF method. According to this method, the fifteen characteristics with the greatest predictive power were: Bilateral cataracts, visual acuity left eye, age, visual acuity right eye, CFH2 genetic variant, history of alcohol use, CFH1 genetic variant, diabetes, glaucoma, pterygium, altered glucose in fasting, cataract left, xerosis, tobacco use, and sex, as shown in Figure 2. This means that those variables are the ones that have the most impact on the diagnosis of AMD.  
In Figure 2, features are listed from high to low according to their relevance and predictive power. The MDGI value of each variable is scaled in the range [0, 100].  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & \multirow{2}{*}{**N (\%)**} & \multicolumn{2}{c}{**Any Cataract**} & \multirow{2}{*}{_p-Value_} \\  & & **Yes** & **No** & \\  & & **(N = 146)** & **(N = 110)** & \\ \hline Demographics & & & & \\ \hline Age, Mean \(\pm\) s & 74.2 \(\pm\) 8.2 & 75.3 \(\pm\) 8.3 & 72.7 \(\pm\) 8.0 & **0.0134** \\ Sex, & & & & 0.5054 \\ Females & 164 (64.0) & 91 (62.3) & 73 (66.4) & \\ Males & 92 (35.9) & 55 (37.7) & 37 (33.6) & \\ \hline Clinical comorbidities & & & & \\ \hline Hypertension & 128 (50.0) & 75 (51.4) & 53 (48.2) & 0.6135 \\ Tobacco use & 52 (20.3) & 32 (21.9) & 20 (18.2) & 0.4620 \\ EtOH use & 23 (9.0) & 20 (13.7) & 3 (2.7) & **0.0024** \\ Type 2 diabetes & 57 (22.3) & 37 (25.3) & 20 (18.2) & 0.1728 \\ Dyslipidemia & 12 (4.7) & 8 (5.5) & 4 (3.6) & 0.4898 \\ \hline Ocular comorbidities & & & & \\ \hline Diabetic retinopathy & 9 (3.5) & 4 (2.7) & 5 (4.6) & 0.4374 \\ Presbyopia & 60 (23.4) & 31 (21.2) & 29 (26.4) & 0.3374 \\ Claudoma & 56 (21.9) & 25 (17.1) & 31 (28.2) & **0.0341** \\ History of any ophthalmic surgery & 91 (35.6) & 84 (57.5) & 7 (6.4) & **<0.0001** \\ \hline Genotype Distribution & & & & **0.0461** \\ \hline CFH1 & & & & **0.0461** \\ CC & 121 (47.3) & 71 (48.6) & 50 (45.5) & \\ CT & 96 (37.5) & 47 (32.2) & 49 (44.6) & \\ TT & 39 (15.2) & 28 (19.2) & 11 (10.0) & \\ \hline CFH2 & & & & 0.7118 \\ CC & 25 (9.8) & 16 (11.0) & 9 (8.2) & \\ CT & 223 (87.1) & 125 (85.6) & 98 (89.1) & \\ TT & 8 (3.1) & 5 (3.4) & 3 (2.7) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the sample by cataract status.  
In the second experiment, we performed a deeper analysis for the estimated fifteen most important variables for RFE with the RF method. The analysis was done in greater depth with the Boruta method [26].  
The results of analyzing the fifteen most important variables according to the random forest method using the Boruta method is shown in Figure 3. The box plots in blue colour represent the Z-score of shadow variables. The red box plot represents the discarded variables, and the green box plot values represent the variables that have been confirmed. According to this analysis of the fifteen variables, bilateral cataracts is the one with the most predictive power to determine if a subject will develop AMD. It is important to notice that this feature results as a good feature to discriminate if a person will have AMD, but this does not mean that bilateral cataracts is a risk factor for the disease.  
Figure 3: Results of analyzing the fifteen most important variables using the Boruta method.  
Figure 2: Results of analyzing all variables using the random forest (RF) method.