Islam June 12, 2022
knitr::opts_chunk$set(fig.path='Figs/')In this analysis, we will explore hospital data, focusing on patient characteristics and outcomes such as survival rates. We will also build a machine learning model to predict hospital death based on various features.
# Load data
data <- read_csv("dataset.csv")## New names:
## Rows: 91713 Columns: 85
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (7): ethnicity, gender, icu_admit_source, icu_stay_type, icu_type, apac... dbl
## (77): encounter_id, patient_id, hospital_id, age, bmi, elective_surgery,... lgl
## (1): ...84
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...84`
# Inspect the structure of the data
glimpse(data)## Rows: 91,713
## Columns: 85
## $ encounter_id <dbl> 66154, 114252, 119783, 79267, 92056, 331~
## $ patient_id <dbl> 25312, 59342, 50777, 46918, 34377, 74489~
## $ hospital_id <dbl> 118, 81, 118, 118, 33, 83, 83, 33, 118, ~
## $ age <dbl> 68, 77, 25, 81, 19, 67, 59, 70, 45, 50, ~
## $ bmi <dbl> 22.73000, 27.42000, 31.95000, 22.64000, ~
## $ elective_surgery <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1~
## $ ethnicity <chr> "Caucasian", "Caucasian", "Caucasian", "~
## $ gender <chr> "M", "F", "F", "F", "M", "M", "F", "M", ~
## $ height <dbl> 180.3, 160.0, 172.7, 165.1, 188.0, 190.5~
## $ icu_admit_source <chr> "Floor", "Floor", "Accident & Emergency"~
## $ icu_id <dbl> 92, 90, 93, 92, 91, 95, 95, 91, 114, 114~
## $ icu_stay_type <chr> "admit", "admit", "admit", "admit", "adm~
## $ icu_type <chr> "CTICU", "Med-Surg ICU", "Med-Surg ICU",~
## $ pre_icu_los_days <dbl> 0.541666667, 0.927777778, 0.000694444, 0~
## $ weight <dbl> 73.9, 70.2, 95.3, 61.7, NA, 100.0, 156.6~
## $ apache_2_diagnosis <dbl> 113, 108, 122, 203, 119, 301, 108, 113, ~
## $ apache_3j_diagnosis <dbl> 502.01, 203.01, 703.03, 1206.03, 601.01,~
## $ apache_post_operative <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1~
## $ arf_apache <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ gcs_eyes_apache <dbl> 3, 1, 3, 4, NA, 4, 4, 4, 4, 4, 4, 4, 4, ~
## $ gcs_motor_apache <dbl> 6, 3, 6, 6, NA, 6, 6, 6, 6, 6, 6, 6, 6, ~
## $ gcs_unable_apache <dbl> 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ gcs_verbal_apache <dbl> 4, 1, 5, 5, NA, 5, 5, 5, 5, 5, 5, 4, 5, ~
## $ heart_rate_apache <dbl> 118, 120, 102, 114, 60, 113, 133, 120, 8~
## $ intubated_apache <dbl> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0~
## $ map_apache <dbl> 40, 46, 68, 60, 103, 130, 138, 60, 66, 5~
## $ resprate_apache <dbl> 36, 33, 37, 4, 16, 35, 53, 28, 14, 46, 1~
## $ temp_apache <dbl> 39.3, 35.1, 36.7, 34.8, 36.7, 36.6, 35.0~
## $ ventilated_apache <dbl> 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0~
## $ d1_diasbp_max <dbl> 68, 95, 88, 48, 99, 100, 76, 84, 65, 83,~
## $ d1_diasbp_min <dbl> 37, 31, 48, 42, 57, 61, 68, 46, 59, 48, ~
## $ d1_diasbp_noninvasive_max <dbl> 68, 95, 88, 48, 99, 100, 76, 84, 65, 83,~
## $ d1_diasbp_noninvasive_min <dbl> 37, 31, 48, 42, 57, 61, 68, 46, 59, 48, ~
## $ d1_heartrate_max <dbl> 119, 118, 96, 116, 89, 113, 112, 118, 82~
## $ d1_heartrate_min <dbl> 72, 72, 68, 92, 60, 83, 70, 86, 82, 57, ~
## $ d1_mbp_max <dbl> 89, 120, 102, 84, 104, 127, 117, 114, 93~
## $ d1_mbp_min <dbl> 46, 38, 68, 84, 90, 80, 97, 60, 71, 59, ~
## $ d1_mbp_noninvasive_max <dbl> 89, 120, 102, 84, 104, 127, 117, 114, 93~
## $ d1_mbp_noninvasive_min <dbl> 46, 38, 68, 84, 90, 80, 97, 60, 71, 59, ~
## $ d1_resprate_max <dbl> 34, 32, 21, 23, 18, 32, 38, 28, 24, 44, ~
## $ d1_resprate_min <dbl> 10, 12, 8, 7, 16, 10, 16, 12, 19, 14, 14~
## $ d1_spo2_max <dbl> 100, 100, 98, 100, 100, 97, 100, 100, 97~
## $ d1_spo2_min <dbl> 74, 70, 91, 95, 96, 91, 87, 92, 97, 96, ~
## $ d1_sysbp_max <dbl> 131, 159, 148, 158, 147, 173, 151, 147, ~
## $ d1_sysbp_min <dbl> 73, 67, 105, 84, 120, 107, 133, 71, 98, ~
## $ d1_sysbp_noninvasive_max <dbl> 131, 159, 148, 158, 147, 173, 151, 147, ~
## $ d1_sysbp_noninvasive_min <dbl> 73, 67, 105, 84, 120, 107, 133, 71, 98, ~
## $ d1_temp_max <dbl> 39.9, 36.3, 37.0, 38.0, 37.2, 36.8, 37.2~
## $ d1_temp_min <dbl> 37.2, 35.1, 36.7, 34.8, 36.7, 36.6, 35.0~
## $ h1_diasbp_max <dbl> 68, 61, 88, 62, 99, 89, 107, 74, 65, 83,~
## $ h1_diasbp_min <dbl> 63, 48, 58, 44, 68, 89, 79, 55, 59, 61, ~
## $ h1_diasbp_noninvasive_max <dbl> 68, 61, 88, NA, 99, 89, NA, 74, 65, 83, ~
## $ h1_diasbp_noninvasive_min <dbl> 63, 48, 58, NA, 68, 89, NA, 55, 59, 61, ~
## $ h1_heartrate_max <dbl> 119, 114, 96, 100, 89, 83, 79, 118, 82, ~
## $ h1_heartrate_min <dbl> 108, 100, 78, 96, 76, 83, 72, 114, 82, 6~
## $ h1_mbp_max <dbl> 86, 85, 91, 92, 104, 111, 117, 88, 93, 1~
## $ h1_mbp_min <dbl> 85, 57, 83, 71, 92, 111, 117, 60, 71, 77~
## $ h1_mbp_noninvasive_max <dbl> 86, 85, 91, NA, 104, 111, 117, 88, 93, 1~
## $ h1_mbp_noninvasive_min <dbl> 85, 57, 83, NA, 92, 111, 117, 60, 71, 77~
## $ h1_resprate_max <dbl> 26, 31, 20, 12, NA, 12, 18, 28, 24, 29, ~
## $ h1_resprate_min <dbl> 18, 28, 16, 11, NA, 12, 18, 26, 19, 17, ~
## $ h1_spo2_max <dbl> 100, 95, 98, 100, 100, 97, 100, 96, 97, ~
## $ h1_spo2_min <dbl> 74, 70, 91, 99, 100, 97, 100, 92, 97, 96~
## $ h1_sysbp_max <dbl> 131, 95, 148, 136, 130, 143, 191, 119, 1~
## $ h1_sysbp_min <dbl> 115, 71, 124, 106, 120, 143, 163, 106, 9~
## $ h1_sysbp_noninvasive_max <dbl> 131, 95, 148, NA, 130, 143, NA, 119, 104~
## $ h1_sysbp_noninvasive_min <dbl> 115, 71, 124, NA, 120, 143, NA, 106, 98,~
## $ d1_glucose_max <dbl> 168, 145, NA, 185, NA, 156, 197, 129, 36~
## $ d1_glucose_min <dbl> 109, 128, NA, 88, NA, 125, 129, 129, 288~
## $ d1_potassium_max <dbl> 4.0, 4.2, NA, 5.0, NA, 3.9, 5.0, 5.8, 5.~
## $ d1_potassium_min <dbl> 3.4, 3.8, NA, 3.5, NA, 3.7, 4.2, 2.4, 5.~
## $ apache_4a_hospital_death_prob <dbl> 0.10, 0.47, 0.00, 0.04, NA, 0.05, 0.10, ~
## $ apache_4a_icu_death_prob <dbl> 0.05, 0.29, 0.00, 0.03, NA, 0.02, 0.05, ~
## $ aids <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ cirrhosis <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ diabetes_mellitus <dbl> 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0~
## $ hepatic_failure <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ immunosuppression <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0~
## $ leukemia <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0~
## $ lymphoma <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ solid_tumor_with_metastasis <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ apache_3j_bodysystem <chr> "Sepsis", "Respiratory", "Metabolic", "C~
## $ apache_2_bodysystem <chr> "Cardiovascular", "Respiratory", "Metabo~
## $ ...84 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
## $ hospital_death <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0~
summary(data)## encounter_id patient_id hospital_id age
## Min. : 1 Min. : 1 Min. : 2.0 Min. :16.00
## 1st Qu.: 32852 1st Qu.: 32830 1st Qu.: 47.0 1st Qu.:52.00
## Median : 65665 Median : 65413 Median :109.0 Median :65.00
## Mean : 65606 Mean : 65537 Mean :105.7 Mean :62.31
## 3rd Qu.: 98342 3rd Qu.: 98298 3rd Qu.:161.0 3rd Qu.:75.00
## Max. :131051 Max. :131051 Max. :204.0 Max. :89.00
## NA's :4228
## bmi elective_surgery ethnicity gender
## Min. :14.85 Min. :0.0000 Length:91713 Length:91713
## 1st Qu.:23.64 1st Qu.:0.0000 Class :character Class :character
## Median :27.66 Median :0.0000 Mode :character Mode :character
## Mean :29.19 Mean :0.1837
## 3rd Qu.:32.93 3rd Qu.:0.0000
## Max. :67.81 Max. :1.0000
## NA's :3429
## height icu_admit_source icu_id icu_stay_type
## Min. :137.2 Length:91713 Min. : 82.0 Length:91713
## 1st Qu.:162.5 Class :character 1st Qu.:369.0 Class :character
## Median :170.1 Mode :character Median :504.0 Mode :character
## Mean :169.6 Mean :508.4
## 3rd Qu.:177.8 3rd Qu.:679.0
## Max. :195.6 Max. :927.0
## NA's :1334
## icu_type pre_icu_los_days weight apache_2_diagnosis
## Length:91713 Min. :-24.94722 Min. : 38.60 Min. :101.0
## Class :character 1st Qu.: 0.03542 1st Qu.: 66.80 1st Qu.:113.0
## Mode :character Median : 0.13889 Median : 80.30 Median :122.0
## Mean : 0.83577 Mean : 84.03 Mean :185.4
## 3rd Qu.: 0.40903 3rd Qu.: 97.10 3rd Qu.:301.0
## Max. :159.09097 Max. :186.00 Max. :308.0
## NA's :2720 NA's :1662
## apache_3j_diagnosis apache_post_operative arf_apache gcs_eyes_apache
## Min. : 0.01 Min. :0.0000 Min. :0.000 Min. :1.000
## 1st Qu.: 203.01 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:3.000
## Median : 409.02 Median :0.0000 Median :0.000 Median :4.000
## Mean : 558.22 Mean :0.2011 Mean :0.028 Mean :3.465
## 3rd Qu.: 703.03 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:4.000
## Max. :2201.05 Max. :1.0000 Max. :1.000 Max. :4.000
## NA's :1101 NA's :715 NA's :1901
## gcs_motor_apache gcs_unable_apache gcs_verbal_apache heart_rate_apache
## Min. :1.000 Min. :0.0000 Min. :1.000 Min. : 30.00
## 1st Qu.:6.000 1st Qu.:0.0000 1st Qu.:4.000 1st Qu.: 86.00
## Median :6.000 Median :0.0000 Median :5.000 Median :104.00
## Mean :5.471 Mean :0.0095 Mean :3.995 Mean : 99.71
## 3rd Qu.:6.000 3rd Qu.:0.0000 3rd Qu.:5.000 3rd Qu.:120.00
## Max. :6.000 Max. :1.0000 Max. :5.000 Max. :178.00
## NA's :1901 NA's :1037 NA's :1901 NA's :878
## intubated_apache map_apache resprate_apache temp_apache
## Min. :0.0000 Min. : 40.00 Min. : 4.00 Min. :32.10
## 1st Qu.:0.0000 1st Qu.: 54.00 1st Qu.:11.00 1st Qu.:36.20
## Median :0.0000 Median : 67.00 Median :28.00 Median :36.50
## Mean :0.1512 Mean : 88.02 Mean :25.81 Mean :36.41
## 3rd Qu.:0.0000 3rd Qu.:125.00 3rd Qu.:36.00 3rd Qu.:36.70
## Max. :1.0000 Max. :200.00 Max. :60.00 Max. :39.70
## NA's :715 NA's :994 NA's :1234 NA's :4108
## ventilated_apache d1_diasbp_max d1_diasbp_min d1_diasbp_noninvasive_max
## Min. :0.0000 Min. : 46.00 Min. :13.00 Min. : 46.00
## 1st Qu.:0.0000 1st Qu.: 75.00 1st Qu.:42.00 1st Qu.: 75.00
## Median :0.0000 Median : 86.00 Median :50.00 Median : 87.00
## Mean :0.3257 Mean : 88.49 Mean :50.16 Mean : 88.61
## 3rd Qu.:1.0000 3rd Qu.: 99.00 3rd Qu.:58.00 3rd Qu.: 99.00
## Max. :1.0000 Max. :165.00 Max. :90.00 Max. :165.00
## NA's :715 NA's :165 NA's :165 NA's :1040
## d1_diasbp_noninvasive_min d1_heartrate_max d1_heartrate_min d1_mbp_max
## Min. :13.00 Min. : 58 Min. : 0.00 Min. : 60.0
## 1st Qu.:42.00 1st Qu.: 87 1st Qu.: 60.00 1st Qu.: 90.0
## Median :50.00 Median :101 Median : 69.00 Median :102.0
## Mean :50.24 Mean :103 Mean : 70.32 Mean :104.7
## 3rd Qu.:58.00 3rd Qu.:116 3rd Qu.: 81.00 3rd Qu.:116.0
## Max. :90.00 Max. :177 Max. :175.00 Max. :184.0
## NA's :1040 NA's :145 NA's :145 NA's :220
## d1_mbp_min d1_mbp_noninvasive_max d1_mbp_noninvasive_min d1_resprate_max
## Min. : 22.00 Min. : 60.0 Min. : 22.00 Min. :14.00
## 1st Qu.: 55.00 1st Qu.: 90.0 1st Qu.: 55.00 1st Qu.:22.00
## Median : 64.00 Median :102.0 Median : 64.00 Median :26.00
## Mean : 64.87 Mean :104.6 Mean : 64.94 Mean :28.88
## 3rd Qu.: 75.00 3rd Qu.:116.0 3rd Qu.: 75.00 3rd Qu.:32.00
## Max. :112.00 Max. :181.0 Max. :112.00 Max. :92.00
## NA's :220 NA's :1479 NA's :1479 NA's :385
## d1_resprate_min d1_spo2_max d1_spo2_min d1_sysbp_max
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 90.0
## 1st Qu.: 10.00 1st Qu.: 99.00 1st Qu.: 89.00 1st Qu.:130.0
## Median : 13.00 Median :100.00 Median : 92.00 Median :146.0
## Mean : 12.85 Mean : 99.24 Mean : 90.45 Mean :148.3
## 3rd Qu.: 16.00 3rd Qu.:100.00 3rd Qu.: 95.00 3rd Qu.:164.0
## Max. :100.00 Max. :100.00 Max. :100.00 Max. :232.0
## NA's :385 NA's :333 NA's :333 NA's :159
## d1_sysbp_min d1_sysbp_noninvasive_max d1_sysbp_noninvasive_min
## Min. : 41.00 Min. : 90.0 Min. : 41.03
## 1st Qu.: 83.00 1st Qu.:130.0 1st Qu.: 84.00
## Median : 96.00 Median :146.0 Median : 96.00
## Mean : 96.92 Mean :148.2 Mean : 96.99
## 3rd Qu.:110.00 3rd Qu.:164.0 3rd Qu.:110.00
## Max. :160.00 Max. :232.0 Max. :160.00
## NA's :159 NA's :1027 NA's :1027
## d1_temp_max d1_temp_min h1_diasbp_max h1_diasbp_min
## Min. :35.10 Min. :31.89 Min. : 37.00 Min. : 22.00
## 1st Qu.:36.90 1st Qu.:36.10 1st Qu.: 62.00 1st Qu.: 52.00
## Median :37.11 Median :36.40 Median : 74.00 Median : 62.00
## Mean :37.28 Mean :36.27 Mean : 75.36 Mean : 62.84
## 3rd Qu.:37.60 3rd Qu.:36.66 3rd Qu.: 86.00 3rd Qu.: 73.00
## Max. :39.90 Max. :37.80 Max. :143.00 Max. :113.00
## NA's :2324 NA's :2324 NA's :3619 NA's :3619
## h1_diasbp_noninvasive_max h1_diasbp_noninvasive_min h1_heartrate_max
## Min. : 37.00 Min. : 22.00 Min. : 46.00
## 1st Qu.: 63.00 1st Qu.: 52.00 1st Qu.: 77.00
## Median : 74.00 Median : 62.00 Median : 90.00
## Mean : 75.81 Mean : 63.27 Mean : 92.23
## 3rd Qu.: 87.00 3rd Qu.: 74.00 3rd Qu.:106.00
## Max. :144.00 Max. :114.00 Max. :164.00
## NA's :7350 NA's :7350 NA's :2790
## h1_heartrate_min h1_mbp_max h1_mbp_min h1_mbp_noninvasive_max
## Min. : 36.00 Min. : 49.00 Min. : 32.0 Min. : 49.00
## 1st Qu.: 69.00 1st Qu.: 77.00 1st Qu.: 66.0 1st Qu.: 77.00
## Median : 82.00 Median : 90.00 Median : 78.0 Median : 90.00
## Mean : 83.66 Mean : 91.61 Mean : 79.4 Mean : 91.59
## 3rd Qu.: 97.00 3rd Qu.:104.00 3rd Qu.: 92.0 3rd Qu.:104.00
## Max. :144.00 Max. :165.00 Max. :138.0 Max. :163.00
## NA's :2790 NA's :4639 NA's :4639 NA's :9084
## h1_mbp_noninvasive_min h1_resprate_max h1_resprate_min h1_spo2_max
## Min. : 32.00 Min. :10.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 66.00 1st Qu.:18.00 1st Qu.: 14.00 1st Qu.: 97.00
## Median : 79.00 Median :21.00 Median : 16.00 Median : 99.00
## Mean : 79.71 Mean :22.63 Mean : 17.21 Mean : 98.05
## 3rd Qu.: 92.00 3rd Qu.:26.00 3rd Qu.: 20.00 3rd Qu.:100.00
## Max. :138.00 Max. :59.00 Max. :189.00 Max. :100.00
## NA's :9084 NA's :4357 NA's :4357 NA's :4185
## h1_spo2_min h1_sysbp_max h1_sysbp_min h1_sysbp_noninvasive_max
## Min. : 0.00 Min. : 75.0 Min. : 53.0 Min. : 75.0
## 1st Qu.: 94.00 1st Qu.:113.0 1st Qu.: 98.0 1st Qu.:113.0
## Median : 96.00 Median :131.0 Median :115.0 Median :130.0
## Mean : 95.17 Mean :133.2 Mean :116.4 Mean :133.1
## 3rd Qu.: 99.00 3rd Qu.:150.0 3rd Qu.:134.0 3rd Qu.:150.0
## Max. :100.00 Max. :223.0 Max. :194.0 Max. :223.0
## NA's :4185 NA's :3611 NA's :3611 NA's :7341
## h1_sysbp_noninvasive_min d1_glucose_max d1_glucose_min d1_potassium_max
## Min. : 53.0 Min. : 73.0 Min. : 33.0 Min. :2.800
## 1st Qu.: 98.0 1st Qu.:117.0 1st Qu.: 91.0 1st Qu.:3.800
## Median :115.0 Median :150.0 Median :107.0 Median :4.200
## Mean :116.5 Mean :174.6 Mean :114.4 Mean :4.252
## 3rd Qu.:134.0 3rd Qu.:201.0 3rd Qu.:131.0 3rd Qu.:4.600
## Max. :195.0 Max. :611.0 Max. :288.0 Max. :7.000
## NA's :7341 NA's :5807 NA's :5807 NA's :9585
## d1_potassium_min apache_4a_hospital_death_prob apache_4a_icu_death_prob
## Min. :2.400 Min. :-1.000 Min. :-1.000
## 1st Qu.:3.600 1st Qu.: 0.020 1st Qu.: 0.010
## Median :3.900 Median : 0.050 Median : 0.020
## Mean :3.935 Mean : 0.087 Mean : 0.044
## 3rd Qu.:4.300 3rd Qu.: 0.130 3rd Qu.: 0.060
## Max. :5.800 Max. : 0.990 Max. : 0.970
## NA's :9585 NA's :7947 NA's :7947
## aids cirrhosis diabetes_mellitus hepatic_failure
## Min. :0e+00 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0e+00 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0e+00 Median :0.0000 Median :0.0000 Median :0.000
## Mean :9e-04 Mean :0.0157 Mean :0.2252 Mean :0.013
## 3rd Qu.:0e+00 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1e+00 Max. :1.0000 Max. :1.0000 Max. :1.000
## NA's :715 NA's :715 NA's :715 NA's :715
## immunosuppression leukemia lymphoma
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.0262 Mean :0.0071 Mean :0.0041
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :715 NA's :715 NA's :715
## solid_tumor_with_metastasis apache_3j_bodysystem apache_2_bodysystem
## Min. :0.0000 Length:91713 Length:91713
## 1st Qu.:0.0000 Class :character Class :character
## Median :0.0000 Mode :character Mode :character
## Mean :0.0206
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :715
## ...84 hospital_death
## Mode:logical Min. :0.0000
## NA's:91713 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0863
## 3rd Qu.:0.0000
## Max. :1.0000
##
read_csv("dataset.csv"): Loads the dataset from a CSV file.glimpse(data): Provides a quick look at the structure of the dataset.summary(data): Summarizes the dataset, giving insights into the distribution of variables.
# Identify features with excessive missing values
missing_counts <- colSums(is.na(data))
large_missing <- missing_counts[missing_counts > 25000]
# Drop columns with excessive missing values and irrelevant columns
data <- data %>%
select(-c(large_missing %>% names(), 'encounter_id', 'icu_admit_source',
'icu_id', 'icu_stay_type', 'patient_id', 'hospital_id'))
# Drop rows with missing 'bmi', 'weight', 'height'
data <- data %>%
filter(!is.na(bmi) & !is.na(weight) & !is.na(height))- Identify Missing Values: We calculate the number of missing
values for each column using
colSums(is.na(data))and filter out columns with excessive missing values (more than 25,000 missing). - Column Removal: We remove columns that are irrelevant to the analysis, such as IDs and columns with excessive missing values.
- Row Filtering: We drop rows that are missing crucial variables
like
bmi,weight, andheightfor further analysis.
# Create a histogram with ggplot2
ggplot(data %>% drop_na(age, gender, hospital_death), aes(x = age, fill = as.factor(hospital_death))) +
geom_histogram(binwidth = 5, position = "dodge") +
facet_wrap(~ gender) +
labs(title = "Histogram of Age and Hospital Death by Gender", x = "Age", fill = "Hospital Death") +
theme_minimal()
- Visualization: We plot histograms to visualize the distribution
of age, hospital deaths, and gender using
ggplot2. The plot is made interactive withggplotly.
# Calculate average hospital death probability by age and gender
age_death <- data %>%
group_by(age, gender) %>%
summarize(hospital_death = mean(hospital_death, na.rm = TRUE))## `summarise()` has grouped output by 'age'. You can override using the `.groups`
## argument.
# Plot average hospital death probability
ggplot(age_death, aes(x = age, y = hospital_death, color = gender)) +
geom_line() +
labs(title = "Average Hospital Death Probability by Age and Gender", x = "Age", y = "Average Hospital Death") +
theme_minimal()## Warning: Removed 3 row(s) containing missing values (geom_path).
- Average Hospital Death by Age: We group the data by age and gender and calculate the average hospital death probability for each group. The result is visualized using a line plot.
# Calculate average hospital death by weight and BMI
weight_death <- data %>%
group_by(weight = round(weight, 0)) %>%
summarize(hospital_death = mean(hospital_death, na.rm = TRUE))
bmi_death <- data %>%
group_by(bmi = round(bmi, 0)) %>%
summarize(hospital_death = mean(hospital_death, na.rm = TRUE))
# Create plots for BMI and Weight
p1 <- ggplot(weight_death, aes(x = weight, y = hospital_death)) +
geom_line() +
labs(title = "Impact of Weight on Hospital Death", x = "Weight", y = "Average Hospital Death") +
theme_minimal()
p2 <- ggplot(bmi_death, aes(x = bmi, y = hospital_death)) +
geom_line() +
labs(title = "Impact of BMI on Hospital Death", x = "BMI", y = "Average Hospital Death") +
theme_minimal()
# Arrange plots side by side
grid.arrange(p1, p2, ncol = 2)
- BMI and Weight Analysis: We analyze the impact of
bmiandweighton hospital death, visualizing the relationship using scatter plots.
# Calculate survival rate by ICU type
ICU_type <- data %>%
mutate(icu_type = recode(icu_type,
'CTICU' = 'CCU-CTICU',
'Cardiac ICU' = 'CCT-CTICU',
'CSICU' = 'SICU')) %>%
group_by(icu_type, age) %>%
summarize(hospital_death = mean(hospital_death, na.rm = TRUE))## `summarise()` has grouped output by 'icu_type'. You can override using the
## `.groups` argument.
# Plot survival rate by ICU type
ggplot(ICU_type, aes(x = age, y = hospital_death, color = icu_type)) +
geom_point() +
labs(title = "Survival Rate at Different ICU Types", x = "Age", y = "Average Hospital Death") +
theme_minimal()## Warning: Removed 6 rows containing missing values (geom_point).
- ICU Type Analysis: This analysis explores survival rates across different ICU types, visualized using scatter plots that differentiate patients by age.
# Plot survival rate by Apache 3J body system
# Calculate survival rate by Apache 3J body system
apache3 <- data %>%
group_by(apache_3j_bodysystem, age) %>%
summarize(size = n(), mean_death = mean(hospital_death, na.rm = TRUE)) %>%
ungroup()## `summarise()` has grouped output by 'apache_3j_bodysystem'. You can override
## using the `.groups` argument.
# Plot survival rate by Apache 3J body system
ggplot(apache3, aes(x = age, y = mean_death, size = size, color = apache_3j_bodysystem)) +
geom_point(alpha = 0.7) +
labs(title = "Survival Rate by Apache 3J Body System", x = "Age", y = "Average Hospital Death") +
theme_minimal()## Warning: Removed 12 rows containing missing values (geom_point).
- Apache 3J Body System Analysis: We examine survival rates across different Apache 3J body systems, with each body system visualized separately.
# Convert columns to the correct class
convert_to_integer <- c('elective_surgery', 'apache_post_operative', 'arf_apache', 'gcs_unable_apache',
'intubated_apache', 'ventilated_apache', 'aids', 'cirrhosis', 'diabetes_mellitus',
'hepatic_failure', 'immunosuppression', 'leukemia', 'lymphoma', 'solid_tumor_with_metastasis')
data <- data %>%
mutate(across(all_of(convert_to_integer), as.integer),
hospital_death = factor(hospital_death, levels = c(0, 1)))- Data Type Conversion: We convert specific columns to integer
type and treat the target variable
hospital_deathas a factor for classification purposes.
# Split data
set.seed(42)
split <- initial_split(data, prop = 0.75)
train_data <- training(split)
test_data <- testing(split)- Train-Test Split: We split the data into training and testing sets using an 75-25 split ratio.
# Preprocessing Recipe
rec <- recipe(hospital_death ~ ., data = train_data) %>%
step_impute_median(all_numeric_predictors()) %>%
step_unknown(all_nominal_predictors()) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>% # Remove zero-variance columns
step_normalize(all_numeric_predictors())- Recipe for Data Preprocessing:
step_impute_median(all_numeric_predictors()): Imputes missing values for numeric predictors using the median.step_unknown(all_nominal_predictors()): Handles unknown levels in categorical variables by assigning them a new level.step_dummy(all_nominal_predictors()): Converts categorical variables into dummy (one-hot encoded) variables.step_zv(all_predictors()): Removes predictors with zero variance, which don’t contribute to model training.step_normalize(all_numeric_predictors()): Normalizes numeric predictors to have mean zero and unit variance.
# Define model
model <- logistic_reg() %>%
set_engine("glm")
# Define workflow
wf <- workflow() %>%
add_recipe(rec) %>%
add_model(model)
# Train model
fit <- fit(wf, data = train_data)- Model Definition: We define a logistic regression model using
the
tidymodelsframework.logistic_reg() %>% set_engine("glm"): Specifies a logistic regression model using the “glm” engine.
- Workflow: We combine the preprocessing recipe and the model into a workflow.
- Model Training: We train the model on the training data using
the
fit()function.
# Evaluate model
results <- fit %>%
predict(new_data = test_data) %>%
bind_cols(test_data) %>%
metrics(truth = hospital_death, estimate = .pred_class)## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
cat("Model: Logistic Regression\n")## Model: Logistic Regression
cat("Accuracy:", results %>% filter(.metric == "accuracy") %>% pull(.estimate), "\n")## Accuracy: 0.925785
- Model Prediction: We generate predictions on the test data and bind the predictions to the actual outcomes.
- Model Evaluation: We compute evaluation metrics, including
accuracy, using the
metrics()function. The accuracy of the logistic regression model is printed.
# Make a prediction for a specific row
row_index <- 91611
dataset_row <- data[row_index, ] %>% select(-hospital_death)
prediction <- predict(fit, new_data = dataset_row)## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
cat("\n------------------------------------\n")##
## ------------------------------------
if (prediction$.pred_class == 0) {
cat("\tPatient Death\n")
} else {
cat("\tPatient Survived\n")
}## Patient Death
cat("\n------------------------------------\n")##
## ------------------------------------
- Specific Prediction: We make a prediction for a specific patient
using the model.
- We extract the row corresponding to a specific patient from the
dataset and remove the
hospital_deathcolumn (as it is the target variable). - The prediction is made, and the result is displayed, indicating whether the patient survived or not.
- We extract the row corresponding to a specific patient from the
dataset and remove the
In this analysis, we explored hospital data, handled missing values, performed exploratory data analysis, and built a logistic regression model to predict patient outcomes. The model was evaluated using accuracy on the test data, and predictions were made for a specific patient. Future work could include trying different models and tuning hyperparameters for improved performance. ```