Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm
Conditions: Ischemic Stroke
Sponsor: Second Affiliated Hospital of Nanchang University

Clinical Research Protocol 
Project Proposal
Scheme name:
Research on the Ischemic Stroke Recurrence
Risk
Prediction
Model
Using
XGBoost
Combined with Convolutional Neural Network
Algorithm
Scheme number:
2021efyB03
Research types:
Observational study
Source
of
funding:The
Second
Affiliated
Hospital
of
Nanchang University
Version number:
V 1.0
Applicant:
Yi Yingping
Phone：
13807089678
Unit:The Second Affiliated Hospital of Nanchang University
Contact
address:No.1
Minde
Road,Nanchang,Jiangxi
Province,China
Email address:
yyp66@126.com
Declaration date:
April 29, 2021

The Second Affiliated Hospital of Nanchang University Clinical
Research Center

3
一.Abstract of the Research Plan (should include research title, research objective, design type,
research subjects, sample size, inclusion criteria, observation indicators, statistical analysis
methods, etc.)
1. Research Title: Study on the Recurrence Risk Prediction Model of Ischemic Stroke Using
XGBoost Combined with Convolutional Neural Network Algorithms.
2. Research Abstract:
Stroke is the leading cause of disability and death among adults in China, with an annual death
toll of approximately 1.5 million and a disability rate of about 75%. About 70% of stroke patients
suffer from ischemic stroke, and the recurrence rates at 3 months, 6 months, and 12 months after
the first ischemic stroke are approximately 10.9%, 13.4%, and 14.7%, respectively. The risk of
death and disability after the recurrence of ischemic stroke is 9.4 times that of the initial stroke.
This project increases the sample size based on the existing ischemic stroke follow-up cohort and
conducts a 1-year follow-up to observe the recurrence situation, mRs scores, and adverse events
after discharge. Combining complex multi-dimensional data such as demographic data, physical
examination, medical history, imaging images, medication information, scale scores, CYP2C19
genotype test results, laboratory examinations, etc.; using convolutional neural networks for
lesion segmentation of ischemic stroke patient imaging and feature extraction; using Cox
regression models to obtain factors influencing recurrence and adverse events; constructing an
ischemic stroke recurrence risk prediction model within 1, 3, 6, and 12 months using the
XGBoost combined with convolutional neural network algorithm (Convolutional Neural
Network, CNN). Exploring the predictive effect of XGBoost on ischemic stroke recurrence risk
at different times and providing new ideas and methods for the prevention and control of major
chronic diseases.
3. Design Type: Cohort Study.
4. Research Subjects: Inpatients with ischemic stroke from the Department of Neurology at the
Second Affiliated Hospital of Nanchang University and more than ten tertiary hospitals in the
province.
5. Sample Size: 2400 cases.
6. Inclusion Criteria:
(1) Inclusion criteria:
1) Aged 18-85; 2) Diagnosed with ischemic stroke or transient ischemic attack (diagnosis in line
with the standards set by the Cerebrovascular Disease Group of the Neurology Society of the
Chinese Medical Association in 2014); 3) Within the acute phase of onset (2 weeks); 4)
Voluntarily participate and sign an informed consent form.
(2)Exclusion criteria:
1)Patients with cancer; 2) Patients with cardiogenic infarction, other causes of cerebral infarction,
or stroke of unknown cause; 3) Patients with hemorrhagic stroke, mixed stroke, and tumor stroke;
4) Patients with severe heart, lung, or liver system diseases; 5) Patients judged by researchers to
have poor compliance and unable to complete long-term follow-up; 6) Patients currently
participating in any clinical trial of investigational drugs or medical devices.
7. Observation Indicators:

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Demographic data, physical examination, medical history, imaging images, medication
information, scale scores, CYP2C19 gene test results, laboratory examinations, etc., mRS scores,
recurrence situation (whether recurrent, number of recurrences, time of recurrence, type of stroke
recurrence, etc.), and adverse events (type of adverse event, severity, start time, stop time, etc.) of
ischemic stroke patients within 1, 3, 6, and 12 months after discharge.
8. Statistical Analysis Methods:
This study uses SPSS25.0 software for statistical analysis. Measurement data that conforms to the
normal distribution are expressed as mean ± standard deviation ( ); Measurement data that do not
conform to the normal distribution are expressed as median (quartile) [M(Q1, Q3)]; Count data
are expressed as frequency and percentage (%). The comparison between count data groups uses
the chi-square test or Fisher's exact probability method; the comparison between measurement
data groups uses the t-test or Mann-Whitney U test. P<0.05 is considered statistically significant.
9. Modeling Methods:
CNN, Logistic, SVM, RF, XGBoost model construction is implemented using PyCharm
(professional v2018.3) software under the Anaconda Python3.6 environment. Data preprocessing
uses the numpy (1.20) and Pandas (1.2.0) packages, CNN image processing model construction
uses the tensorflow (2.1.1) architecture, data analysis and model construction use Scikit-learn
(0.23.1).
Brain imaging feature extraction: Introducing deep convolutional neural network models such as
UNet++, FCN, AlexNet, etc., to process brain imaging images, while considering the natural 3D
attributes of brain imaging images, using 3D convolution, pooling to capture changes in brain
imaging between continuous slices; comparing the effects of different deep convolutional neural
network models, selecting the optimal model as the method for extracting brain imaging features.
Prediction model construction: The variables screened by Cox regression with statistical
differences are fused with the imaging features extracted by CNN. The fused data is divided into
training sets, validation sets, and test sets, using a ten-fold cross-validation training method,
respectively establishing machine learning-based ischemic stroke recurrence risk prediction
models (research models: XGBoost; control models: Logistic, SVM, RF) within 1, 3, 6, and 12
months after discharge, continuously optimizing the model effects on the validation set until the
optimal.
Model Evaluation: After the model effect reaches the ideal effect, compare the performance
evaluation indicators of the research model and the control model on the test set (AUC value,
sensitivity, time efficiency, etc.). AUC, sensitivity, and time efficiency are used to evaluate the
comprehensive predictive ability, accuracy rate, and prediction efficiency of the model,
respectively.

"Research on the Ischemic Stroke Recurrence Risk
Prediction Model Using XGBoost Combined with
Convolutional Neural Network Algorithm"
Informed Consent Form
Part One
Subject Instructions
Research Introduction:
You are invited to participate in the project titled "Research on the Predictive
Model of Recurrent Risk of Ischemic Stroke Using XGBoost Combined with
Convolutional Neural Networks." You have been invited to join this study because
you meet the research criteria.
This informed consent form provides you with some information to help you
decide whether to participate in this clinical study. Your participation in this study is
voluntary. If you agree to join this study, please read the following instructions.
Please read carefully, and if you have any questions, please ask the researcher
responsible for the study.
Research Purpose:
Cerebrovascular disease is a serious illness that leads to death and disability,
especially ischemic stroke, which has become a severe threat to human health,
significantly affecting the quality of life of patients and imposing a heavy burden on
families and society. The preferred treatment for preventing neurological dysfunction
and recurrence of cerebrovascular disease, leading to further disability, is antiplatelet
therapy. However, about 25%-60% of patients exhibit resistance to aspirin or
clopidogrel, rendering treatment ineffective. Previous studies have found that
CYP2C19 gene polymorphism and urinary 11dhTxB2 can effectively reflect
resistance to commonly used clinical antiplatelet drugs and have a certain predictive
effect on the occurrence and development of the disease. This study aims to collect
various clinical data from subjects, including demographic information (age, gender,
marital status, etc.), past medical history (previous stroke, hypertension, diabetes,
dyslipidemia, etc.), laboratory tests (complete blood count, liver and kidney function,
lipid profile, etc.), imaging studies (CT, MRI, etc.), clinical medication (aspirin,
clopidogrel, etc.), clinical diagnosis (pulmonary infection, urinary tract infection, etc.),
antiplatelet drug resistance testing (CYP2C19 gene and urinary 11-dhTxB2) results,
neurological function assessment (NIHSS/mRS scores), follow-up data (adverse
events, recurrence), etc., to explore the risk of recurrence in ischemic stroke and
construct corresponding predictive models. This can provide new methods for
predicting ischemic stroke recurrence and assisting in clinical decision-making for
major chronic diseases.
Research process and methods:
Since you have been diagnosed with ischemic stroke, we invite you to participate
and further verify whether you meet the inclusion criteria. Researchers will obtain
your basic and disease-related information during your hospital stay and schedule
follow-ups for you at 1 month, 3 months, 6 months, and 12 months after discharge to

understand your treatment and changes in your condition. If you agree to participate
in this study, we will assign a number to each participant and establish a medical
record.
Possible benefits of the study:
If you agree to participate in the study and meet the inclusion criteria of the
research protocol, analyzing your medical records will help provide important basis
for your individualized treatment, and also provide useful information for the study of
the disease, ultimately bringing blessings to you and other patients in similar
situations.
Research on risk and discomfort:
Privacy issues:
If you decide to participate in this study, your participation and personal
information during the trial will be kept confidential. Information that can identify
you will not be disclosed to anyone outside the research team without your permission.
All members of the research team and the research sponsors are required to keep your
identity confidential. To ensure that the study is conducted in accordance with
regulations, members of the ethics review committee may, when necessary, review
your personal information at our institution. When the results of this study are
published, no personal information about you will be disclosed.
Free Exit:
As a participant, you can at any time access information and progress related to
this study, and voluntarily decide whether to continue participating or not. After
joining, regardless of whether any harm occurs or how severe it is, you can choose to
notify the researcher at any time to request to withdraw from the study. Your data will
not be included in the study results, and your medical treatment and rights will not be
affected by this. If continuing to participate in the study would cause you serious
harm, the researcher would also stop the study.
However, during the study period, please provide accurate information about
your medical history and current health status: inform the study doctor whether you
have recently participated in other studies or are currently participating in other
studies. If you do not comply with the study plan, or if any injury related to the study
occurs, or for any other reason, the study physician can terminate your participation in
this study.
Contact information:
If you have any questions related to this study, or if you experience any
discomfort or injury during the research, or if you have any concerns about the rights
of participants in this study, you can contact the study doctor.
The trial protocol has been approved by the hospital ethics committee for
implementation, and if there is any violation of the research protocol during the trial,
participants can directly lodge a complaint with the hospital ethics committee. Contact
number: 0791-86209562.
Part Two Informed Consent Signature
I have read this informed consent form, understood the purpose, content, risks,
and benefits of the study, and I am aware of this clinical research. I voluntarily agree

to participate in this study.
Subject's name:
Subject signature:
Signature date:
Contact phone number:
Legal representative's name:
Relationship to the subject:
Legal representative's signature:
Signature date:
Contact phone number:
Researchers declare:
I have explained to the patient the details of this study, including their rights as
well as potential benefits and risks, and have given them a copy of the signed
informed consent form.
Researcher's name:
Researcher's signature: