8 Use case 1

8.1 Introduction

In the preceding chapters we have looked at four methods for supervised learning: Nearest Neighbours (chapter 5), Support Vector Machines (chapter 6), Decision Trees (chapter 7) and Random Forests (chapter 7). In this chapter we will consider how we choose the best method for a particular data set.

8.2 Problem: automated detection of malaria

Park HS, Rinehart MT, Walzer KA, Chi J-TA, Wax A (2016) Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells. PLoS ONE 11(9): e0163045. https://doi.org/10.1371/journal.pone.0163045

Bright-field microscopy images: (A) uninfected RBC (B-D) RBCs with malaria parasite in early trophozoite, late trophozoite, and schizont stages respectively (scale bars = 5μm). Source: Park et al (2016) https://doi.org/10.1371/journal.pone.0163045

Uninfected RBC and RBCs infected by P. falciparum in early trophozoite, late trophozoite, and schizont stages represented respectively as: (A-D) OPL maps, (E-F) OPL maps from different viewpoint (scale bars = 5μm). Source: Park et al (2016) https://doi.org/10.1371/journal.pone.0163045

N.B. These are images of typical specimens. There is actually considerable variation in the appearance of each stage. See the supplementary material for the paper: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0163045#sec014

Park et al (2016) performed cell segmentation on these images and extracted 23 morphological parameters from each RBC:

Max optical path length (OPL)
Mean OPL
Median OPL
Optical volume
Minor axis length
Major axis length
Perimeter
Area
Elongation
Equivalent diameter
Eccentricity
Standard deviation OPL
Variance OPL
Interquartile range OPL
Skewness
Kurtosis
Mean symmetry
Min symmetry
Maximum gradient
Mean gradient
Centroid vs centre of mass
Upper quartile mean OPL
Upper quartile mean gradient

8.3 Challenges

Find the best model for diagnosing malaria (discriminating infected from uninfected RBCs)
Find the best model for staging infection (classifying infection stage: uninfected, early trophozoite, late trophozoite, schizont).

8.4 Getting started

8.4.1 Load data

load("data/malaria/malaria.RData")

Inspect objects that have been loaded into R session

ls()

## [1] "infectionStatus" "morphology"      "stage"

class(morphology)

## [1] "data.frame"

dim(morphology)

## [1] 1237   23

names(morphology)

##  [1] "Area"                        "Major Axis Length"          
##  [3] "Minor Axis length"           "Eccentricity"               
##  [5] "Mean OPL"                    "Max OPL"                    
##  [7] "Median OPL"                  "Std OPL"                    
##  [9] "Skewness"                    "Kurtosis"                   
## [11] "Variance OPL"                "IQR OPL"                    
## [13] "Optical volume"              "Centroid vs. center of mass"
## [15] "Elongation"                  "Upper quartile OPL"         
## [17] "Perimeter"                   "Equivalent diameter"        
## [19] "Max gradient"                "Mean gradient"              
## [21] "Upper quartile gradient"     "Min symmetry"               
## [23] "Mean symmetry"

str(morphology)

## 'data.frame':	1237 obs. of  23 variables:
##  $ Area                       : num  60.5 60.4 53 57.1 72.8 ...
##  $ Major Axis Length          : num  8.84 9.18 8.66 8.64 10.64 ...
##  $ Minor Axis length          : num  8.71 8.39 7.82 8.41 8.72 ...
##  $ Eccentricity               : num  0.172 0.406 0.431 0.231 0.573 ...
##  $ Mean OPL                   : num  111.5 105.6 90.5 119.5 94.5 ...
##  $ Max OPL                    : num  190 175 152 224 147 ...
##  $ Median OPL                 : num  121 123 104 128 110 ...
##  $ Std OPL                    : num  56 49.7 41.3 62 41.8 ...
##  $ Skewness                   : num  -0.368 -0.591 -0.577 -0.268 -0.692 ...
##  $ Kurtosis                   : num  1.8 1.92 1.95 1.83 2.09 ...
##  $ Variance OPL               : num  3136 2466 1709 3845 1744 ...
##  $ IQR OPL                    : num  100.5 86.5 71.5 107.6 68.9 ...
##  $ Optical volume             : num  6.74 6.38 4.8 6.82 6.88 ...
##  $ Centroid vs. center of mass: num  0.087 0.0557 0.0174 0.125 0.0215 ...
##  $ Elongation                 : num  1.02 1.09 1.11 1.03 1.22 ...
##  $ Upper quartile OPL         : num  176 157 134 193 137 ...
##  $ Perimeter                  : num  28.8 28.9 27 28.1 31.9 ...
##  $ Equivalent diameter        : num  8.77 8.77 8.22 8.52 9.63 ...
##  $ Max gradient               : num  14.7 13.4 12.3 17.5 12.3 ...
##  $ Mean gradient              : num  7.59 6.21 5.88 8.37 5.1 ...
##  $ Upper quartile gradient    : num  12.56 11.08 10.09 14.01 9.55 ...
##  $ Min symmetry               : num  0.995 0.976 0.966 0.984 0.903 ...
##  $ Mean symmetry              : num  0.997 0.987 0.978 0.991 0.949 ...

class(infectionStatus)

## [1] "factor"

summary(as.factor(infectionStatus))

##   infected uninfected 
##        824        413

class(stage)

## [1] "factor"

summary(as.factor(stage))

## early trophozoite  late trophozoite          schizont        uninfected 
##               173               314               337               413

morphology is a data.frame of the morphological parameters describing the red blood cells.

infectionStatus a factor identifying a red blood cell as either infected or uninfected.

stage a factor giving the stage of infection: early trophozoite, late trophozoite, schizont or uninfected.

8.4.2 Model comparison

Explore how CARET’s resamples function can help you compare models:

?resamples

## No documentation for 'resamples' in specified packages and libraries:
## you could try '??resamples'

8.5 Solutions

We have provided worked examples in appendix G. Can you come up with better solutions?