Cancer prediction via machine learning

1. Screening for Cancer Using a Learning
Internet Advertising System
Publication date: March 2020.
ACM Journals:
ACM Transactions on Computing for Healthcare
Durr-e-Nayab
Mphil (02072213017)

2. ABSTRACT
2

3. “
Using search engine queries and advertising systems
to identify individuals likely to have suspected cancer
improves the effectiveness of cancer diagnosis and
healthcare utilization.
3

4. INTRODUCTION
Search engine queries have proven to be valuable data for
understanding:
o real-world experiences,
o including medical concerns,
o utilized for tracking infectious diseases,
o exploring the link between diet and chronic pain,
o and identifying early indicators of diseases.
4

5. 5
Medical data collection often relies on anonymous sources, making inferences
about users' medical status necessary.
SIU disclose their condition in search
queries
Consisting of more females and
younger people

6. 6
SIU
Geographic
Variability
to identify potential medical
conditions in users. While these
methods allowed for a larger and
more diverse cohort, they lacked
clinical information about the
users, limiting the certainty of
disease inference.

7. The current work demonstrates for the first
time that the ads-serving platform can be
used to target populations at risk for early
diagnosis
7

8. CONTRIBUTION
8

9. 9
Clinically verified questionnaires used to estimate
likelihood of suspected cancer diagnosis
Correlation between questionnaire scores and past
search engine queries demonstrated potential
prediction of suspected cancer
Learning capabilities of advertising systems utilized to
identify more individuals likely to have suspected
cancer

10. METHOD
10

11. FOCUSED ON THREE TYPES OF CANCER
Lung Breast Colon
11

12. 12
Users recruited through targeted ads related
to specific cancer diagnosis
Click on ads
Redirected to a dedicated website
Administer clinically validated
questionnaires
Collect completed questionnaire scores

13. First Study
 Bing ads system
 privileged access to search
system data
 Budget 15$ per day
Second Study
 Google ads system
 Not privileged access to
search system data
 Budget 15$ per day
13

14. RECRUITMENT
14

15. 15
Users were recruited through ads displayed using the respective ads
system.
Recruitment ad
“symptoms of <cancer type>”
“signs of <cancer type>”
“Diagnosis <cancer type>”
“Questionnaire <cancer type>”
“ quiz <cancer type>”
Were shown when

16. 16
Ads contained one of the following three titles:
“<cancer type> Do you have it?”
“<cancer type>Think you have it?”
“<cancer type>- Worried you have
it?”
The text of the ads was “Click here to check if you should see
a doctor” (or physician)
All ads were shown with equal
probability

17. Questionnaires
▣ People who clicked on these ads were referred to a
specially designed website
▣ Questionnaire developed based on the UK National
Institute for Health and Care Excellence (NICE)
17

18. 18
ALGORITHM 1: A simplified flow chart of the cancer questionnaire, based on NICE guideline
_______________________________________________________________________________
Result: Suspected cancer score (SCS)
if you are 30 years or over then
if you have <symptoms according to specific cancer i.e. lung, colon, and breast cancer>then
SCS = High;
else
SCS = Low;
end
end
if you are 50 years or over then
if you have <symptoms according to specific cancer i.e. lung, colon, and breast cancer>then
SCS = High;
else
SCS = Low;
end
end

19. SCS RATE
▣ High SCS
advised to consult an oncologist
within 2 weeks
▣ Low SCS
advised that their symptoms
were not commonly associated
with cancer but that they should
see a medical doctor if the
symptoms were persistent or
worrying.
19

20. STUDY 1
20
Campaign performance:
Recruitment ad
Were shown
(159,170)
Clicked
2,899
1,285 questionnaires were started 681 were
completed

21. 21
Fig. 1. Clickthrough rates on ads by cancer type and age
group.

22. 22
It took an average of 126 seconds to complete the
questionnaires.
Total data analyzed
288 people
185
lung
81
colon
22
breast

23. 23
Prediction of questionnaire
outcome
A combined model was
initially used to screen for all
cancers together. The ROC
curve showed that the area
under the curve (AUC) was
0.66, indicating that it is
possible to identify
individuals who are highly
likely to have the suspected
cancer.

24. When Trained Separately
0.74
colon
0.50
breast
0.56
lung
24
AUCs for the different cancers

25. 25
STUDY 2
25
Campaign performance:
Recruitment ad
Were shown
(70,586)
Clicked
(6,484)

26. Clickthrough Rates
2,917 people began the questionnaire and 1,049 completed it
The study focused on tracking the conversion rate over
time, which refers to the percentage of individuals who
clicked on the ads and were found to have a high SCS.
Approximately 1 out of 9 individuals who clicked on the ads
were suspected to have cancer.

27. 27
The campaign analyzed the keywords that triggered the ads and
the demographic information of the users.
The average conversion rates
 Breast 11%,
 Colon 9%, and
 Lung 9%, respectively.

28. 28
Study 2 demonstrated that the advertising system
successfully learned to identify individuals with suspected
cancer and optimized its performance through keyword
selection and demographic targeting.

29. 29
The suggested approach has the potential to screen for
severe medical conditions in marginalized populations
and alleviate concerns for individuals who do not have
suspected cancer but are anxious about their symptoms.
Nevertheless, additional research is required to validate
its effectiveness and assess its cost-efficiency.