cICIC International ⃝2024
ISSN 1881-803X

ICIC Express Letters
Volume 18, Number 6, June 2024

pp. 615–624

DEVELOPMENT OF ASSISTANCE SYSTEM TO DIAGNOSE
CHANGES IN KIDNEY FUNCTION USING MACHINE LEARNING
Yuki Yamaguchi1 , Noritaka Shigei1,∗ , Masanobu Miyazaki2
Yoichi Ishizuka3 , Shinichi Abe4 , Tomoya Nishino4 and Hiromi Miyajima1
1

Graduate School of Science and Engineering
Kagoshima University
1-21-40 Korimoto, Kagoshima City, Kagoshima 890-0065, Japan{ k6072255; k2356323 }@kadai.jp; ∗ Corresponding author: shigei@ibe.kagoshima-u.ac.jp
2

Miyazaki Medical Clinic
3-12 Shiratorimachi, Nagasaki City, Nagasaki 852-8042, Japanmsnbmiya@gmail.com
3

Graduate School of Engineering
Nagasaki University
1-14 Bunkyo, Nagasaki City, Nagasaki 852-8521, Japan
isy2@nagasaki-u.ac.jp
4

Department of Nephrology
Nagasaki University Hospital
1-7-1 Sakamoto, Nagasaki City, Nagasaki 852-8501, Japan{ s-abe; tnishino }@nagasaki-u.ac.jp

Received August 2023; accepted October 2023
Abstract. To reduce the labor required for physicians to make diagnosis from timeseries data on kidney function from speciﬁc health checkups, this paper proposes 1) cooperative learning in which a sequence of creating a ﬂowchart (FC) and training machinelearning (ML) models is iteratively repeated and 2) a decision assistance system presenting suggestion based on the created FC and ML models. The cooperative learning aims to
reduce the labor for creating FC and preparing supervised data. The decision assistancesystem aims to achieve both evidence-based judgments and accuracy. The eﬀectiveness ofthe proposed framework is demonstrated in an example implementation using real data.
Keywords: Kidney function, Time series data, Trend, Machine learning, Flowchart

1. Introduction. The number of chronic dialysis patients in Japan exceeds 340,000,
and the number is increasing year by year [1]. One of the social issues is to reduce thenumber of dialysis patients due to chronic kidney disease (CKD) since this will lead toan increase in healthy life expectancy and a reduction in medical costs. Early detectionof deteriorating kidney function (KF) and early initiation of treatment are important toprevent chronic kidney disease. Therefore, a major challenge is how to identify patients
at high risk of KF deterioration and how to create a system to provide health guidance
and referral to specialists.
Some municipal governments and medical associations hold case review meetings for
speciﬁc health checkups and prepare comments for family doctors and examinees in orderto encourage patients who need treatment to be examined by nephrologists. However, thejudgment and preparation of comments are largely manual work by physicians and publichealth nurses and require much labor. In order to predict the deterioration of KF, it isimportant to grasp the trend of KF from time series data. However, it is not easy to diagnose the deterioration of renal function because the values of physical examinations may
DOI: 10.24507/icicel.18.06.615

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ﬂuctuate up and down depending on various factors, regardless of the trend. Althougha ﬂowchart-like criterion would help to reduce the burden, it is diﬃcult to establish an
appropriate criterion in the situation above. Furthermore, in such cases, the creation of
the ﬂowchart itself would be a diﬃcult task.
There have been many studies on machine learning (ML) using CKD data. However,most of these studies, such as in [2, 3, 4], are conducted using data from the UCI machinelearning repository [5]. This dataset is non-time series data consisting of 250 CKD patientsand 150 non-CKD patients and is mainly used for binary classiﬁcation. The research by
Ventrella et al. in [6] uses time series data to examine the problem of 2- to 4-classiﬁcation
to predict the time to dialysis. However, it does not explicitly determine trends in KF,
and what it uses is inpatient data and not data from health screening systems. Unlike
previous studies, this study determines the trend of renal function in medical checkup
data.
There are two issues in applying ML. One of the challenges in applying ML is to prepare
suﬃcient supervised data. 1) Manual annotation of patient data by medical specialists
requires a large amount of time and eﬀort. In addition, ML may not always be basedon medical evidence. 2) The output process of ML and deep learning is a black box. It
cannot be logically explained to physicians and patients. Therefore, this study proposes
the use of ﬂowcharts as a solution to both of these problems.
The purpose of this study is to reduce the amount of labor involved in selecting highrisk groups and preparing commentary. We propose 1) cooperative learning in which asequence of creating a ﬂowchart (FC) and training an ML model is iteratively repeated
and 2) a decision assistance system presenting suggestion based on the created FC and ML
models. The cooperative learning aims to reduce the labor for creating FC and preparing
supervised data. The decision assistance system aims to achieve both evidence-based
judgments and accuracy. These proposals solve the above described two issues in applying
ML. The eﬀectiveness of the proposed cooperative learning is demonstrated by showingthat supervised data can be prepared with less annotation work and that the accuracies ofFC and ML improve. Furthermore, the eﬀectiveness of the proposed judgment method isdemonstrated by showing that ML can provide better results for some cases than FC. The
rest of this paper is organized as follows. Section 2 describes the diagnosis of trends in KF
that this study addresses. Section 3 describes the methods used in this study to determine
trends in KF using ML. Sections 4 and 5 propose a framework for cooperative learningand a judgment suggestion system, respectively. Section 6 demonstrates the eﬀectivenessof our proposal. Finally, Section 7 is the conclusion of this paper.
2. CKD Prevention Based on Speciﬁc Health Checkups.
2.1. Speciﬁc health checkups and speciﬁc health guidance. Some municipalities
in Japan provide health guidance based on speciﬁc health checkups. The speciﬁc healthcheckup focuses on metabolic syndrome, which can be taken once a year by people aged
40-74 years to prevent lifestyle-related diseases. In addition, for those who are at highrisk of developing lifestyle-related diseases and for those who are expected to be highly
eﬀective in preventing lifestyle-related diseases by improving their lifestyle, public healthnurses and dietitians provide speciﬁc health guidance to review their lifestyle habits.
2.2. CKD prevention eﬀorts based on speciﬁc health checkups. CKD is characterized by persistent kidney damage and KF decline that progresses to end-stage renal failure requiring dialysis therapy or kidney transplantation. The number of patientson chronic dialysis continues to increase, posing a signiﬁcant healthcare economic problem. CKD also increases the risk of cardiovascular diseases such as myocardial infarction,
stroke, heart failure, and death [7]. The onset of CKD is closely related to lifestyle diseases and age-related decline in KF, making the family physician the center of care forICIC EXPRESS LETTERS, VOL.18, NO.6, 2024

617

CKD patients. However, CKD includes renal diseases such as IgA nephropathy that require treatment by nephrologists or specialized medical institutions in the community.
Therefore, family physicians, nephrologists, and specialized medical institutions need tocooperate in treating CKD patients [7].
One of co-authors, a nephrologist, participates in a case study group using healthcheckup data, in which nephrologists and diabetes specialists, the government, and medical associations collaborate [8]. In this study group, health checkup data are used to
identify high-risk groups for CKD and to develop recommendations for health guidance
and referrals to specialists. The health checkup data are time-series data covering 1 to
5 years. The advice consists of the status of KF, urinary protein, blood glucose, bloodpressure, etc., and whether or not a referral to a specialist is required.
Glomerular ﬁltration rate (GFR) is used to diagnose and classify the severity of CKD. It
represents the amount of wastes, such as creatinine and inulin, that the kidneys eliminate
as urine. GFR can be measured by clearance testing, which is not easily performed in an
outpatient setting. Therefore, the estimated glomerular ﬁltration rate (eGFR) calculated
from gender, serum creatinine Cr, and age α is generally used. For Japanese, eGFR
(ml/min/1.73m2 ) is calculated as follows [9]: For male, eGFR = 194 × Cr−1.094 × α−0.287 ,and for female, eGFR = 194 × Cr−1.094 × α−0.287 × 0.739.

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2.3. Diagnosis of kidney function trends. In preparing the advice described in Section 2.2, it is necessary to appropriately capture trends in KF. In our initial study, thetime series data of eGFR for each examinee were classiﬁed into the seven classes of trends
as shown in Figure 1. R6 is assigned when the number of checkups is two and KF isdecreasing; R7 is assigned when the number of checkups is one and KF is low.

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R6: KF is unstable

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R7: KF is low for one’s age

Figure 1. Examples of eGFR time-series data for seven classes of trends
in kidney function
In progress of our study, we reviewed the classiﬁcation classes and considered two cases,
six-class and four-class as follows. In the case of six-class, the data of R7, which are nottime series data, were deleted. In the case of four-class, in addition to that, R2 was merged
into R3 and R6 into R4, resulting in four classes of R1, R3, R4, and R5.
In the health checkup data, the number of checkups and the duration of checkups varyfrom examinee to examinee, and there is a problem of missing values. In addition, it isdiﬃcult to make appropriate judgments based only on simple conditions such as slope intime-series data, where the series length varies and ﬂuctuates.

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3. Estimation of Kidney Function Trends Using Machine Learning. This section
describes the machine learning-based trend estimation method proposed in [10] for the
problem described in Section 2.3. The method is used in this study. This previous study
proposed data preprocessing to deal with diﬀerent numbers of examinations and diﬀerent
periods of examinations and three ensemble learning-based methods using the Gradient
Boosting Decision Tree (GBDT) as models for ML. This study uses LightGBM for theﬁnal ML model. The data used to determine the trend of KF are the health checkup dataof eGFR for the maximum examination period of Y years: x1 , x2 , . . ., and xY . Here,xy is the value of eGFR of Y − y years ago, and xy = NA if Y − y years ago is not
examined. Let SY = {y|xy ̸= NA} be the set of years of examination. In addition to
the eGFR values x1 , x2 , . . ., and xY , the explanatory variables are the eGFR’s diﬀerence∆ = xY − xminy∈SY y , the regression line’s slope a given by Equation (1), the number of
examinations N = |SY |, and the examination period T = Y + 1 − miny∈SY y.
∑∑∑
N y∈SY yxy − y∈SY y y∈SY xy
(1)
a=
(∑
)2
∑
y
N y∈SY y 2 −y∈SY
If there are years in the examination period T in which no examination is taken, theexplanatory variable xy for those years is linearly interpolated using the values before andafter the examination. That is, for all y > miny′ ∈SY y ′ such that xy = NA, the value ofthe medical examination is given by the following equations. xy = x− + (x+ − x− )(y −y − )/(y + − y − ), where y − and x− are the year and the value of the medical checkup
immediately before year y ∈ SY and y + and x+ are the year and the value of the medical
checkup immediately after year y ∈ SY .
GBDT has shown good accuracy in various applications, and LightGBM is a framework
for implementing GBDT. This previous study uses LightGBM as a baseline model for ML.
Decision trees classify data from the root to leaves based on the given features. GBDT isan ensemble learning method that creates decision tree models sequentially and combinesthese models. The created decision tree models improve accuracy by learning the residuals
between the terget values and the sum of the predictions of all models up to the previous decision tree model as the loss function. LightGBM has a shorter computation timethan other frameworks, because it incorporates gradient-based one-side sampling (GOSS),
which excludes from training data with small contribution to accuracy improvement, andexclusive feature bundling, which eﬀectively reduces the number of features.
4. Cooperative Learning between Machine Learning and Flowchart Creation.
It is diﬃcult to create a ﬂowchart for a classiﬁcation task for which clear decision criteria
are unknown. On the other hand, in ML, the preparation of teacher data required fortraining models is labor-intensive in the annotation process. We propose a framework
for cooperative learning between ML and ﬂowchart creation as a method to solve both ofthese problems.
4.1. General algorithm of the proposed framework. The outline of the proposed
framework is shown in Figure 2. In the proposed framework, Step 1 begins with theannotation of a tiny amount of data by experts of the target domain. This work may
utilize existing data produced by the experts. In Step 2, the worker observes this data,
extracts the main conditions, and creates a ﬂowchart. In Step 3, annotation work on theincreased data is performed by assigning the judgment results of the ﬂowchart as labels
for the data. In Step 4, an ML model is trained by using the data in Step 3. In Step 5,
experts review only the data for which the judgment result of the ML model diﬀers fromthat of the ﬂowchart, correct the labels and modify the ﬂowchart. After that, Steps 3, 4
and 5 are repeated a certain number of times to update the ML model and modify the
ﬂowchart.

ICIC EXPRESS LETTERS, VOL.18, NO.6, 2024

START

S.4: Review & revise FC

S.1: Annota on work

S.5: Train ML model

S.2: Create FC
S.3: Apply FC

S.6: Apply ML model
S.7.1: Review discrepant
ML and FC data

619

S.7.3: Revise FC
Yes
S.7.2 Needto modify
FC?

No
END

Figure 2. The outline of the proposed framework of cooperative learningThis ﬂow reduces the expert’s eﬀort for the following reasons. The expert only needs
to process a tiny number of data in Step 1 for the annotation work, and in Step 5, theexpert only needs to review a relatively small number of data. In addition, the expert’s
eﬀort in creating the ﬂowchart is small because it is mainly created by the worker basedon the data. On the other hand, the annotation work for ML can be semi-automated fora large amount of data since the ﬂowchart is used to perform the annotation work.
The general algorithm for this framework is as follows.
Step 1: Experts of the target domain perform the annotation work on a small dataset.
Step 2: Workers create a ﬂowchart (FC) by the annotation data.
Step 3: Apply the FC to a larger dataset and obtain the judgment result.
Step 4: Experts review and revise the judgment result in Step 3. Workers revise the FC
according to the revised judgment result.
Step 5: Apply the FC to a larger and/or the same dataset and obtain the judgment
results of the FC as labeled datasets.
Step 6: Train an ML model (or ML models with cross-validation) by using revised labeleddataset or dataset labeled by FC, and apply the ML model (or ML models) to the largest
dataset so far to obtain judgment result by ML models.
Step 7: Experts review only the data for which the judgment result of the ML modelor models diﬀers from that of the FC and correct the labels. If any revisions are made,workers modify the FC and go to Step 5. Otherwise, terminate the algorithm.
4.2. Implemented ﬂow of the proposed framework. The proposed framework described in Section 4.1 was implemented using three diﬀerent datasets: a small dataset Da
consisting of 28 cases, a medium dataset Db consisting of 310 cases, and a large dataset
Dc consisting of 3157 cases. The speciﬁc ﬂow is shown below.
Step 1: A nephrologist assigns one of the seven classes from R1 to R7 to each case in
the small dataset Da consisting of 28 cases based on comments made by physicians andpublic health nurses. The obtained labeled dataset of Da is referred to as JD a .
Step 2: Workers create an FC F1 according to JD a .
Step 3: For each case in the medium dataset Db consisting of 405 cases, by applying F1 ,one of the seven classes is assigned as a label. The labeled dataset of Db by F1 is referred
to as JF b1 .
Step 4: The nephrologist reviews and revises the labels of JF b1 , where the classes oflabels are reduced from the seven classes to the six classes of R1∼R6. The revised labeleddataset of Db is referred to as JD b1 . According to JD b1 , the worker revises F1 as F2 .
Step 5: For each case in Db , by applying F2 , one of the six classes R1∼R6 is assigned asa label. As a result, a labeled dataset JF b2 is obtained for Db .
Step 6: All cases of Db are judged by ML models provided from leave-one-out crossvalidation (LOOCV) with JD b1 of the training data set. The obtained ML models andthe judgment result of Db by Mb1 are referred to as Mb1 and JM b1 , respectively.
Step 7: The nephrologist reviews only cases where JD b1 , JF b2 , and JM b1 did not match,and revises JD b1 as JD b2 . According to JD b2 , the worker revises F2 as F3 .

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Step 5 (2nd): For each case in Db and Dc consisting of 3157 cases, by applying F3 , oneof the six classes R1∼R6 is assigned as a label. The obtained labeled datasets of Db and
Dc by F3 are referred to as JF b3 and JF c3 , respectively.
Step 6 (2nd): An ML model Mb2 is trained by using JD b2 as the training dataset. All
cases of Dc are judged by the ML model Mb2 , and the judgment result JM c2 on Dc by
Mb2 is obtained.
Step 7 (2nd): The nephrologist reviews only cases where JF c3 and JM c2 did not match,and creates the labeled dataset JD c3 for Dc , where the classes of labels are reduced to thefour classes of R1, R3, R4, and R5. According to JD c3 , the worker revises F3 as F4 .
Step 5 (3rd): For each case in Dc , by applying F4 , one of the four classes is assigned asa label. The obtained labeled datasets of Dc by F4 are referred to as JF c4 .
Step 6 (3rd): All cases of Dc are judged by ML models provided from 5-fold crossvalidation with JF c4 of the training data set. The obtained ML models and the judgment
result of Dc are referred to as Mc4 and JM c4 , respectively.
Step 7 (3rd): The ﬂow completes with the ﬁnal FC F4 , FC’s judgment JF c4 , ML models
Mc4 and ML’s judgment JM c4 .
5. Judgment Suggestion Using Machine Learning and Flowchart. In the judgment of trends in KF, although the ﬂowchart has clear criteria, there are cases where thephysician’s conclusion diﬀers from the ﬂowchart’s. On the other hand, although machinelearning can make ﬂexible judgments that cannot be made with the ﬂowchart, the basis
for such conclusions is unclear. To address these issues, we propose a system that assists
physicians in making decisions by combining ﬂowchart judgments and machine learning
judgments.
Using the ﬁnal ﬂowchart F4 and machine learning model M4 constructed in the ﬂow presented in Section 4.2, we develop a system that presents information to help the physician
make a decision. The LightGBM is used as the machine learning model, and its decision
result can output the probability for each class in addition to the decision class. In presenting information, these probabilities are also utilized. The proposed system shows thefollowing information related to judgment: 1) The judgment result of FC F4 and 2) M4 ’s
predicted probabilities of classes R1, R3, R4, and R5.
Figure 3 shows an example of information presented by the proposed system. In thetable of “Suggestion by FC and ML”, the four classes of R1, R3, R4, and R5 are displayedin descending order of M4 ’s predicted probabilities. In column “FC”, the judgment of F4
is marked with “*”. If the FC’s decision agrees with the ML’s one having the highest
predicted probability, then the physician may simply adopt that decision. Otherwise,
the physician may make a selection based on his or her own thinking from those thathave higher prediction probabilities. This procedure of decision making would allow thephysician to make a prompt decision.
eGFR by year
Year 2016 2017 2018 2019 2020
eGFR 81.0 57.0 61.1 54.6 47.8
Suggestion by FC and ML
FC Probability Trend*
54.6 % R5. Rapidly decreasing
38.2 % R4. Decreasing
26.8 % R3. Slow but steadily decreasing
4.5 % R1. Preserved

Figure 3. An example of information presented by the suggestion system

ICIC EXPRESS LETTERS, VOL.18, NO.6, 2024

Table 1. Evaluation results on ﬂowcharts F1 , F2 , F3 , and F4(a) For F1 when ground truth is JD b1

R1
R2
R3
R4
R5
R6
Accuracy

Precision
0.82940.00000.00000.77780.16670.00000.8097

Recall F1-score Support
0.9959 0.9050
244
0.0000 0.0000
90.0000 0.0000
27
0.4667 0.5833
150.5000 0.2500
20.0000 0.0000
13310(b) For F2 when ground truth is JD b2

R1
R2
R3
R4
R5
R6
Accuracy

Precision
0.97500.83330.60530.80000.16670.50000.8903

Recall F1-score Support
0.9590 0.9669
244
0.5556 0.6667
90.8519 0.7077
27
0.5333 0.6400
150.5000 0.2500
20.3846 0.4348
13310(c) For F3 when ground truth is JD b2

R1
R2
R3
R4
R5
R6
Accuracy

Precision
0.9556
0.41670.45450.80000.16670.50000.8226

Recall F1-score Support
0.8811 0.9168
244
0.5556 0.4762
90.9259 0.6098
27
0.5333 0.6400
150.5000 0.2500
20.0769 0.1333
13310(d) For F3 (4 classes) when ground truth is JD b2

R1
R3
R4
R5
Accuracy

Precision
0.9556
0.46270.83330.16670.8290Recall F1-score Support
0.8811 0.9168
244
0.8611 0.6019
36
0.3571 0.5000
28
0.5000 0.2500
2310(e) For F4 when ground truth is JD b2

R1
R3
R4
R5
Accuracy

Precision
0.95690.52000.55560.00000.8484

Recall F1-score Support
0.9098 0.9328
244
0.7222 0.6047
36
0.5357 0.5455
28
0.0000 0.0000
2
310

621

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6. Evaluation and Consideration.
6.1. Accuracy of ﬂowcharts. In this section, we evaluate the accuracy of ﬂowcharts
and ML models at each step of the ﬂow presented in Section 4.2.
The FCs F1 , F2 , F3 , and F4 use the number of examinations N , the last year’s eGFR
xY , the eGFR change per year (xY − x1 )/(Y − 1), the regression line’s slope a given by
Equation (1), and the number of up/down movements in eGFR as explanatory variables.
The evaluation results on FCs F1 , F2 , F3 , and F4 are shown in Table 1. In the evaluation,
since the cases of N = 1 are excluded, there is no data with the label R7. From the results,
it can be observed that the accuracy of F2 is improved from F1 . In particular, F1 hasscores of 0 for R2, R3, and R6, which is due to inappropriate conditions. This defect has
been ﬁxed in F2 . The accuracy of F3 is lower than F2 . This is due to the fact that theconditions for F3 are more concise, giving priority to interpretability. At this point, weconsidered changing from 6 classes to 4 classes and conﬁrmed that this would improveaccuracy. F4 is more accurate than F3 .
6.2. Accuracy of machine learning models. Table 2 shows the accuracy of ML models assuming JF c4 is ground truth (GT). The ML models for JM c2 and JM c4 are logistic
regression and LightGBM, respectively. JM c2 has much worse accuracy for R2, R4, R5,
and R6. In contrast, in the ﬁnal model JM c4 , which is for four-class classiﬁcation, theaccuracy of all classes has much improved and achieved accuracy of 98%. On the other
hand, in [6], using in-hospital data, ML is applied to predicting the time to dialysis as afour-class classiﬁcation problem, achieving 89% accuracy for extremely randomized trees.
Table 2. Confusion matrices for ML models(a) For JM c2 when ground truth is JF c3

ML\GT
R1
R2
R3
R4
R5
R6
Rec.
F1

R1
R2
R3
R4
R5
R6
1995
58
203
20
6
1
63
3
7
5
3
3
30
13
133
24
6
0
24
7
37
46
16
2
0
0
0
4
30
0
23
1
34
11
32
4
0.934 0.037 0.321 0.418 0.323 0.400
0.903 0.036 0.429 0.380 0.472 0.070

Prec.
0.874
0.036
0.646
0.348
0.882
0.038
Acc.
0.777

(b) For JM c4 when ground truth is JF c4

ML\GT
R1
R3
R4
R5
Rec.
F1

R1
R3
R4
R5
2441
7
6
0
15
366
7
0
12
7
261
4
0
0
4
27
0.989 0.963 0.939 0.871
0.992 0.953 0.929 0.871

Prec.
0.995
0.943
0.919
0.871
Acc.
0.980

Table 3 shows the confusion matrices of the ﬁnal ML model Mc4 assuming JD c4 is GT,
where JD c4 is nephrologist’s judgment. Table 3(b) is the result when the ﬁrst and second
rankings are considered as the correct answer. It can be observed that most of the correct
answers are contained in the top two predictions of the ﬁnal ML model Mc4 .

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623

Table 3. Confusion matrices of ML model Mc4 when ground truth is JD c4(a) When only the ﬁrst ranking is considered as
the correct answer

ML\GT
R1
R3
R4
R5

R1 R3 R4 R52449 23
0
14 371 30
12
4 268 001
3 27(b) When the ﬁrst and second rankings are
considered as the correct answer

ML\GT
R1
R3
R4
R5

R1 R3 R4 R52472 0
0
0
0
376 0
0
3
1 277 001
0 27

Table 4. Confusion matrices for data where FC and ML did not match(a) Confusion matrix of ML and FC

ML\FC
R1
R3
R4
R5

R1 R3 R4 R5
0
7
4
0
15 0
7
0
11 7
0
4
0
0
4
0

(b) Confusion matrix of ML and JD c4

ML\GT
R1
R3
R4
R5

R1 R3 R4 R5
8
2
1
0
14 5
3
0
11 4
7
0
0
1
3
0

(c) Confusion matrix of ML’s 1st and 2nd ranking and JD c4

ML\GT
R1
R3
R4
R5

R1 R3 R4 R5
30 0
00
0 10 00
3
1 14 00
1
0
0

Table 4 shows the confusion matrices for data where FC and ML did not match. According to Table 4(a), in 59 out of 3157 cases, F4 and Mc4 decisions were in disagreement.
Table 4(b) shows that Mc4 gave the correct answer in 20 out of the 59 cases. Furthermore,
Table 4(c) shows that in 54 out of the 59 cases, the correct decision is among the top two
predictions of Mc4 .
7. Conclusion. In this paper, we proposed cooperative learning and a decision assistancesystem to reduce the labor required for physicians to make diagnosis of kidney function.
The evaluation results demonstrated that 1) collaborative learning improves the accuracyof the ﬂowcharts and ML models and 2) the combined system of ﬂowchart and ML models
created by cooperative learning presents useful information to assist physicians in making
decisions. In future works, we will apply the proposed system for actual review meetings
to evaluate and improve the system.
Acknowledgments. We thank Ms. Y. Anami of Health Support Labo for her eﬀorts inanonymizing the speciﬁc health checkup data.
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