Machine Learning-based Colorectal Cancer Prediction using Global Dietary Data
Hanif Abdul Rahman
SOCR University of Michigan and Universiti Brunei Darussalam
hanifr@umich.edu; hanif.rahman@ubd.edu.bn
Mohammad Ashraf Ottom
SOCR University of Michigan and Yarmouk University
maottom@umich.edu; ottom.ma@yu.edu.jo
Ivo D. Dinov
SOCR University of Michigan
library(arules)
library(plotly)
library(dplyr)
library(arulesViz)
a.colrec <- read.transactions("df_trans_text2.csv", sep = ",", skip = 1, rm.duplicates=TRUE)
colrec_rule <- apriori(a.colrec, parameter=list(support=0.01, confidence=0.25, minlen=2))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.25 0.1 1 none FALSE TRUE 5 0.01 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 629
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[3743 item(s), 62911 transaction(s)] done [0.06s].
## sorting and recoding items ... [15 item(s)] done [0.00s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [19 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].sortedRule <- sort(colrec_rule)
x1 <- sortedRule@quality$support
y1 <- sortedRule@quality$confidence
z1 <- sortedRule@quality$lift
col1 <- sortedRule@quality$count
ruleNames <- paste0("Rule", c(1:length(sortedRule@quality$support)))
plot_ly(x = ~x1, y = ~y1, z = ~z1, color = ~z1, name=ruleNames) %>%
add_markers() %>%
layout(title=paste0("Arule Support-Confidence-Lift Plot (for all ", length(sortedRule@quality$support), " rules)"),
scene = list(xaxis = list(title = 'Support'),
yaxis = list(title = 'Confidence'),
zaxis = list(title = 'Lift'))) %>%
hide_colorbar()fi_rules<-subset(colrec_rule, items %in% "colrec_ca")
inspect(fi_rules)## lhs rhs support confidence coverage lift count
## [1] {Salad dressing, oil and vinegar, homemade} => {colrec_ca} 0.01071355 1.0000000 0.01071355 1.00000 674
## [2] {Lettuce, salad with assorted vegetables including tomatoes and/or carrots, no dressing (Lettuce salad, NFS)} => {colrec_ca} 0.01438540 1.0000000 0.01438540 1.00000 905
## [3] {FAT BLEND FAT 75% FORTIFIED BREGOTT} => {colrec_ca} 0.02161784 1.0000000 0.02161784 1.00000 1360
## [4] {Vegetable oil, olive} => {colrec_ca} 0.01543450 1.0000000 0.01543450 1.00000 971
## [5] {Egg, whole, fried, with fat (Scrambled egg, no milk added)} => {colrec_ca} 0.01586368 1.0000000 0.01586368 1.00000 998
## [6] {Vegetable oil, canola and soybean} => {colrec_ca} 0.01789830 1.0000000 0.01789830 1.00000 1126
## [7] {Beef, ground, medium, broiled} => {colrec_ca} 0.01827979 1.0000000 0.01827979 1.00000 1150
## [8] {HARD CHEESE FETT 28%} => {colrec_ca} 0.02867543 1.0000000 0.02867543 1.00000 1804
## [9] {Butter, regular} => {colrec_ca} 0.02824625 1.0000000 0.02824625 1.00000 1777
## [10] {Cheese, cheddar} => {colrec_ca} 0.03024908 1.0000000 0.03024908 1.00000 1903
## [11] {Salad dressing, mayonnaise, commercial, regular} => {colrec_ca} 0.02894565 1.0000000 0.02894565 1.00000 1821
## [12] {Egg, chicken, whole, fresh or frozen, raw} => {colrec_ca} 0.03619399 1.0000000 0.03619399 1.00000 2277
## [13] {Margarine, tub, composite} => {colrec_ca} 0.04479344 1.0000000 0.04479344 1.00000 2818
## [14] {Shortening, household, unspecified vegetable oil} => {colrec_ca} 0.06151547 1.0000000 0.06151547 1.00000 3870
## [15] {Egg, chicken, whole, fresh or frozen, raw,
## Egg, whole, fried, with fat (Scrambled egg, no milk added)} => {colrec_ca} 0.01193750 1.0000000 0.01193750 1.00000 751
## [16] {colrec_ca,
## Egg, whole, fried, with fat (Scrambled egg, no milk added)} => {Egg, chicken, whole, fresh or frozen, raw} 0.01193750 0.7525050 0.01586368 20.79088 751
## [17] {colrec_ca,
## Egg, chicken, whole, fresh or frozen, raw} => {Egg, whole, fried, with fat (Scrambled egg, no milk added)} 0.01193750 0.3298199 0.03619399 20.79088 751plot(sort(fi_rules, by="lift"), method = "graph", engine = "htmlwidget",
control=list(main = list(title="Grouped Matrix for the 14 Fentanyl-associated Rules")))## Available control parameters (with default values):
## itemCol = #CBD2FC
## nodeCol = c("#EE0000", "#EE0303", "#EE0606", "#EE0909", "#EE0C0C", "#EE0F0F", "#EE1212", "#EE1515", "#EE1818", "#EE1B1B", "#EE1E1E", "#EE2222", "#EE2525", "#EE2828", "#EE2B2B", "#EE2E2E", "#EE3131", "#EE3434", "#EE3737", "#EE3A3A", "#EE3D3D", "#EE4040", "#EE4444", "#EE4747", "#EE4A4A", "#EE4D4D", "#EE5050", "#EE5353", "#EE5656", "#EE5959", "#EE5C5C", "#EE5F5F", "#EE6262", "#EE6666", "#EE6969", "#EE6C6C", "#EE6F6F", "#EE7272", "#EE7575", "#EE7878", "#EE7B7B", "#EE7E7E", "#EE8181", "#EE8484", "#EE8888", "#EE8B8B", "#EE8E8E", "#EE9191", "#EE9494", "#EE9797", "#EE9999", "#EE9B9B", "#EE9D9D", "#EE9F9F", "#EEA0A0", "#EEA2A2", "#EEA4A4", "#EEA5A5", "#EEA7A7", "#EEA9A9", "#EEABAB", "#EEACAC", "#EEAEAE", "#EEB0B0", "#EEB1B1", "#EEB3B3", "#EEB5B5", "#EEB7B7", "#EEB8B8", "#EEBABA", "#EEBCBC", "#EEBDBD", "#EEBFBF", "#EEC1C1", "#EEC3C3", "#EEC4C4", "#EEC6C6", "#EEC8C8", "#EEC9C9", "#EECBCB", "#EECDCD", "#EECFCF", "#EED0D0", "#EED2D2", "#EED4D4", "#EED5D5", "#EED7D7", "#EED9D9", "#EEDBDB", "#EEDCDC", "#EEDEDE", "#EEE0E0", "#EEE1E1", "#EEE3E3", "#EEE5E5", "#EEE7E7", "#EEE8E8", "#EEEAEA", "#EEECEC", "#EEEEEE")
## precision = 3
## igraphLayout = layout_nicely
## interactive = TRUE
## engine = visNetwork
## max = 100
## selection_menu = TRUE
## degree_highlight = 1
## verbose = FALSE# plot(fi_rules, method="graph", measure = "support", engine="htmlwidget", # nodeCol=rainbow(14),
# shading = "lift", control = list(verbose = TRUE))Reference to Apriori protocol: