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--- neuroimagen:bioface_atn [2021/09/09 09:16]
osotolongo [What we got so far]
+++ neuroimagen:bioface_atn [2021/09/12 10:23]
osotolongo [Métodos no lineales]
@@ Line 183: / Line 183: @@
 library("ADNIMERGE")
 pop <- adnimerge[adnimerge$VISCODE=="bl" & (adnimerge$DX=="Dementia" | adnimerge$DX=="CN"),];
-pop$ND = ifelse(pop$DX == "Dementia", 1, 0)
+pop$ND = as.factor(ifelse(pop$DX == "Dementia", 1, 0))
 xt <- pop[, c("Hippocampus", "Entorhinal", "Ventricles", "MidTemp", "AGE", "ICV", "ND")]
 </code>
@@ Line 206: / Line 206: @@
 <code>
+setwd("/home/data/bioface")
 base <- read.csv("bf_data.csv")
 base$Hippocampus = base$Left.Hippocampus + base$Right.Hippocampus
@@ Line 223: / Line 224: @@
 <code>
-base$post <- predict(classifier_cl, newdata = base, type="raw)
+base$post <- predict(classifier_cl, newdata = base, type="raw")
 </code>
+^ Clasificación ^ Probabilidades de N+ ^
+| {{ :neuroimagen:bioface_nplus_classifier.png?600 | my pretty cool neurodegeneration classifier output }} | {{ :neuroimagen:nplus_probabilities_histogram.png?600 | how the probabilities are distributed? }} |
+Y esto voy a hacerlo para FACEHBI también para compararlo,
+<code>
+setwd("/home/data/facehbi")
+read.csv("face_data.csv") -> face
+face$Hippocampus = face$Left.Hippocampus + face$Right.Hippocampus
+face$Entorhinal = face$lh.entorhinal.GrayVol + face$rh.entorhinal.GrayVol
+face$Ventricles <- face$Left.Inf.Lat.Vent + face$Right.Inf.Lat.Vent + face$Left.Lateral.Ventricle + face$Right.Lateral.Ventricle
+face$MidTemp = face$lh.middletemporal.GrayVol + face$rh.middletemporal.GrayVol
+face$ICV = face$eTIV
+face$ND <- predict(classifier_cl, newdata = face)
+face$post <- predict(classifier_cl, newdata = face, type="raw")
+</code>
+^ Clasificación ^ Probabilidades de N+ ^
+| {{ :neuroimagen:facehbi_nbayes_classifier.png?600 | my pretty cool neurodegeneration classifier output (FACEHBI)}} | {{ :neuroimagen:facehbi_nplus_prob_histogram.png?600 |how the probabilities are distributed?}} |
+==== Métodos no lineales ====
+Vamos a intentar con métodos no lineales. Ojo, estos  métodos no tienen interpretación probabilistica. Son construcciones matemáticas que funcionan.
+=== random Forest ===
+** BIOFACE **
+<code>
+rf_cl <- randomForest(ND ~ ., data = xt, na.action = na.omit)
+base$ND <- predict(rf_cl, newdata = base)
+base$post <- predict(rf_cl, newdata = base, type="prob")
+plot(base$AGE, base$Hippocampus, main = "Hippocampus Volume versus Age", xlab="Age", ylab="HV", pch=19, col=ifelse(base$ND==1,"red","green"))
+hist(base$post[,2], col="red", density = 50, probability=TRUE, breaks = 10, main="Probabilities profile", xlab = "Probability of N+")
+</code>
+^ Clasificación ^ Probabilidades de N+ ^
+| {{ :neuroimagen:rf_classifier_bioface.png?600 |}} | {{ :neuroimagen:rf_prob_profile_bioface.png?600 |}} |
+**FACEHBI**
+<code>
+face$ND <- predict(rf_cl, newdata = face)
+face$post <- predict(rf_cl, newdata = face, type = "prob")
+plot(face$AGE, face$Hippocampus, main = "Hippocampus Volume versus Age", xlab="Age", ylab="HV", pch=19, col=ifelse(face$ND==1,"red","green"))
+hist(face$post[,2], col="red", density = 50, probability=TRUE, breaks = 10, main="Probabilities profile", xlab = "Probability of N+")
+</code>
+^ Clasificación ^ Probabilidades de N+ ^
+| {{ :neuroimagen:rf_classifier_facehbi.png?600 |}} | {{ :neuroimagen:rf_prob_profile_facehbi.png?600 |}} |
+=== support-vector machine ===
+**BIOFACE**
+<code>
+svm_cl <- svm(ND ~ ., data = xt, na.action = na.omit)
+base$ND <- predict(svm_cl, newdata = base)
+plot(base$AGE, base$Hippocampus, main = "Hippocampus Volume versus Age", xlab="Age", ylab="HV", pch=19, col=ifelse(base$ND==1,"red","green"))
+</code>
+{{ :neuroimagen:svm_classifier_bioface.png?600 |}}
+** FACEHBI **
+<code>
+face$ND <- predict(svm_cl, newdata = face)
+plot(face$AGE, face$Hippocampus, main = "Hippocampus Volume versus Age", xlab="Age", ylab="HV", pch=19, col=ifelse(face$ND==1,"red","green"))
+</code>
+{{ :neuroimagen:svm_classifier_facehbi.png?600 |}}

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