Application of Machine Learning to Predict Weight Loss in Overweight, and Obese Patients on Korean Medicine Weight Management Program

Eunjoo Kim; Young-Bae Park; Kahye Choi; Young-Woo Lim; Ji-Myung Ok; Eun-Young Noh; Tae Min Song; Jihoon Kang; Hyangsook Lee; Seo-Young Kim; Kim, Eunjoo; Park, Young-Bae; Choi, Kahye; Lim, Young-Woo; Ok, Ji-Myung; Noh, Eun-Young; Song, Tae Min; Kang, Jihoon; Lee, Hyangsook; Kim, Seo-Young

doi:10.13048/jkm.20015

JKM > Volume 41(2); 2020 > Article

Kim, Park, Choi, Lim, Ok, Noh, Song, Kang, Lee, and Kim: Application of Machine Learning to Predict Weight Loss in Overweight, and Obese Patients on Korean Medicine Weight Management Program

Original Article

The Journal of Korean Medicine 2020; 41(2): 58-79.

Published online: June 1, 2020

DOI: https://doi.org/10.13048/jkm.20015

Application of Machine Learning to Predict Weight Loss in Overweight, and Obese Patients on Korean Medicine Weight Management Program

Eunjoo Kim¹

, Young-Bae Park¹

, Kahye Choi¹^{, 2}

, Young-Woo Lim¹^{, 2}

, Ji-Myung Ok¹^{, 2}

, Eun-Young Noh³

, Tae Min Song⁴

, Jihoon Kang⁵

, Hyangsook Lee⁶

, Seo-Young Kim¹^{, 2}^{, *}

¹Nubebe Mibyeong Research Institute

²Nubebe Korean Medical Clinic

³Department of Medical Science of Meridian, College of Korean Medicine, Graduate School, Kyung Hee University

⁴Department of Health Management, Sahmyook University

⁵Department of Business Administration, Korea Polytechnic University

⁶Department of Anatomy, College of Korean Medicine, Kyung Hee University

Correspondence to：김서영 누베베 한의원. 경기도 성남시 분당구 성남대로 926번길 10, 탑빌딩 2층, Tel: +82-31-702-0045, Fax: +82-31-701-8993, E-mail: woori4025@hanmail.net

Received May 8, 2020 Revised May 22, 2020 Accepted May 25, 2020

Abstract

Objectives

The purpose of this study is to predict the weight loss by applying machine learning using real-world clinical data from overweight and obese adults on weight loss program in 4 Korean Medicine obesity clinics.

Methods

From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 kg/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting.

Results

Prediction accuracy was increased from 1^st to 2^nd and 3^rd analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1^st (54.69%), 2^nd (73.52%), and 3^rd (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1^st (0.640), 2^nd (0.816), and 3^rd (0.939) prediction analysis based on reduced weight.

Conclusions

The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.

Keywords: Machine learning, Obesity, Weight loss, Artificial intelligence

Fig. 1

Flowchart of dataset for analysis

Fig. 2

Data analytics lifecycle

Fig. 3

A schematic diagram of prediction analyses of weight loss.

The analysis for predicting weight loss was divided into three parts, and the weight loss at each time point refers to the change from the initial point of treatment to the point of weight report.

Fig. 4

Receiver operating characteristics (ROC) curves

Table 1A

Independent Variables Used in the First Analysis (n=25,988)

Variables
Age (years)		36.34 ± 10.5

Weight (kg)		72.69 ± 10.50

BMI (kg/m²)		27.60 ± 3.04

Patients with medication dose change (n, %)	Stable	21,590 (83.08)
Patients with medication dose change (n, %)	Increased	4,398 (16.92)

Gender (n, %)	Male	2,292 (8.82)
Gender (n, %)	Female	23,696 (91.18)

Dietary habits (n, %)*	Light eating	749 (2.88)
	Binge eating	10,817 (41.62)
	Nighttime eating	8,197 (31.54)

Weight loss experience (n, %)	None	6,575 (25.3)
	Diet, exercise only or weight loss drug for less than 3 months	10,247 (39.43)
	Weight loss drug over 3 months	9,166 (35.27)

Diseases (n, %)*	High blood pressure	1,790 (6.89)
	Anemia	1,922 (7.4)
	Diabetes	676 (2.6)
	Hypothyroidism	598 (2.3)
	Hyperthyroidism	293 (1.13)
	Gastritis	846 (3.26)
	Reflux esophagitis	490 (1.89)
	Hyperlipidemia	610 (2.35)
	Low back pain	794 (3.06)

* multiple choices allowed

Data are expressed as n (%) for categorical variables and mean ± SD for continuous variables.

Table 1B

Independent Variables Used in the 2^nd Analysis (n=6,304)

	Variables		Added Variables in 2^nd Analysis

				Variables

Age (years)		37.09±10.25	Weight2 (kg)		69.81±10.01
Weight (kg)		72.51±10.36	BMI2 (kg/m²)		26.55±2.94
BMI (kg/m²)		27.58±3.02	Prescription Period1–2 (days)		25.72±5.19

Patients with medication dose change (n, %)	Stable	5217(82.76) (1^st)	Reduced BMI1–2 (kg/m²)		1.02±0.46
		→3,847(61.02) (2^nd)
	Increased	1087(17.24) (1^st)	Reduced Rate1–2 (%)		3.71±1.6
		→2,458(38.98) (2^nd)

Gender (n, %)	Male	527 (8.34)	Reduced Weight1–2 (kg)		2.7±1.25

	Female	5,777 (91.64)	Symptoms of discomfort 1–2 (n, %)*	Gastro-intestinal system	673 (10.68)

Dietary habits (n, %)*	Light eating	198 (3.14)		Central and peripheral nervous system	497 (7.88)
	Binge eating	2,593 (41.13)		Psychiatric Symptoms	204 (3.24)
	Nighttime eating	1,888 (29.95)		Autonomic nervous system	117 (1.86)

Weight loss experience (n, %)	None	1,656 (26.27)		Others	205 (3.25)

	Diet, exercise only or weight loss drug for less than 3 months	2,449 (38.85)	Satisfaction with weight loss 1–2 (n, %)	Good	3,143 (49.86)
	Weight loss drug over 3 months	2,199 (34.88)	Satisfaction with weight loss 1–2 (n, %)	Fair	2,296 (36.42)

Diseases (n, %)*	High blood pressure	487 (7.58)		Poor	865 (13.72)

	Anemia	445 (7.06)	Satiety and appetite suppression 1–2 (n, %)	Good	2,691 (42.69)
	Diabetes	160 (2.54)		Fair	1381 (21.91)
	Hypothyroidism	148 (2.35)		Poor	2232 (35.41)

	Hyperthyroidism	88 (1.4)	Attendance2 (n, %)		3,793 (60.17)
	Gastritis	141 (2.24)
	Reflux esophagitis	67 (1.06)
	Hyperlipidemia	162 (2.57)
	Low back pain	478 (3.16)

* multiple choices allowed

Data are expressed as n (%) for categorical variables and mean ± SD for continuous variables.

Table 1C

Independent Variables Used in the 3^rd Analysis (n=833)

	Variables
Age (years)					38.19 ± 10.42
Weight (kg)					73.96 ± 10.26
BMI (kg/m²)					28.3 ± 3.14
Patients with medication dose change (n, %)	Stable		648(77.79) (1^st)→481(57.74) (2^nd)→324(38.90) (3^rd)
Patients with medication dose change (n, %)	Increased		185(22.21) (1^st)→352(42.26) (2^nd)→509(61.10) (3^rd)
Gender (n, %)	Male				51 (8.82)
Gender (n, %)	Female				782 (91.18)
Dietary habits (n, %)*	Light eating				23 (2.76)
	Binge eating				349 (41.9)
	Nighttime eating				242 (29.05)
Weight loss experience (n, %)	None				215 (25.81)
	Diet, exercise only or weight loss drug for less than 3 months				305 (36.61)
	Weight loss drug over 3 months				313 (37.58)
Diseases (n, %)*	High blood pressure				72 (8.64)
	Anemia				49 (5.88)
	Diabetes				26 (3.12)
	Hypothyroidism				30 (3.6)
	Hyperthyroidism				8 (0.96)
	Gastritis				18 (2.16)
	Reflux esophagitis				10 (1.2)
	Hyperlipidemia				27 (3.24)
	Low back pain				28 (3.36)

Added Variables in the 2nd Analysis			Added Variables in the 3rd Analysis

	Variables			Variables

Weight2 (kg)		71.21 ± 9.81	Weight3 (kg)		68.97 ± 9.51
BMI2 (kg/m²)		27.25 ± 3.02	BMI3 (kg/m²)		26.39 ± 2.94
Prescription Period1–2 (days)		25.49 ± 5.24	Prescription Period1–3 (days)		58.57 ± 8.17
			Prescription Period2–3 (days)		33.09 ± 6.53
Reduced BMI1–2 (kg/m²)		1.05 ± 0.46	Reduced BMI1–3 (kg/m²)		1.91 ± 0.71
			Reduced BMI2–3 (kg/m²)		0.86 ± 0.48
Reduced Rate1–2 (%)		3.70 ± 1.54	Reduced Rate1–3 (%)		6.71 ± 2.31
			Reduced Rate2–3 (%)		3.13 ± 1.74
Reduced Weight1–2 (kg)		2.75 ± 1.25	Reduced Weight1–3 (kg)		4.99 ± 1.93
			Reduced Weight2–3 (kg)		2.24 ± 1.28
Symptoms of discomfort 1–2 (n, %)*	Gastro-intestinal system	79 (9.48)	Symptoms of discomfort 2–3 (n, %)*	Gastro-intestinal system	72 (8.64)
	Central and peripheral nervous system	80 (9.6)		Central and peripheral nervous system	38 (4.56)
	Psychiatric Symptoms	27 (3.24)		Psychiatric Symptoms	24 (2.88)
	Autonomic nervous system	14 (1.68)		Autonomic nervous system	14 (1.68)
	Others	42 (5.04)		Others	29 (3.48)
Satisfaction with weight loss 1–2 (n, %)	Good	463 (55.58)	Satisfaction with weight loss 2–3 (n, %)	Good	472 (56.66)
	Fair	284 (34.09)		Fair	264 (31.69)
	Poor	86 (10.32)		Poor	97 (11.64)
Satiety and appetite suppression 1–2 (n, %)	Good	377 (45.26)	Satiety and appetite suppression 2–3 (n, %)	Good	343 (41.18)
	Fair	186 (22.33)		Fair	181 (21.73)
	Poor	270 (32.41)		Poor	309 (37.09)
Attendance2 (n, %)		586 (70.35)	Attendance3 (n, %)		616 (73.95)

* multiple choices allowed

Data are expressed as n (%) for categorical variables and mean ± SD for continuous variables.

Table 2

Model Performance for Quartile Classification Bench Mark

Reduced Rate						Reduced Weight

1^st Bench mark		DT1	RF1	LR1	ANN1	1^st Bench mark		DT1	RF1	LR1	ANN1
Upper 25%	4.87%	74.91%	74.91%	74.91%	74.78%	Upper 25%	3.54kg	75.68%	75.70%	75.70%	76.12%
50%	3.75%	53.41%	54.73%	54.26%	55.01%	50%	2.68kg	59.47%	60.18%	59.05%	60.02%
Lower 25%	2.66%	74.80%	74.80%	74.70%	75.08%	Lower 25%	1.90kg	75.27%	75.27%	75.29%	75.41%

2^nd Bench mark		DT2	RF2	LR2	ANN2	2^nd Bench mark		DT2	RF2	LR2	ANN2

Upper 25%	8.21%	78.81%	80.50%	80.39%	80.17%	Upper 25%	5.94kg	82.51%	82.51%	81.13%	82.46%
50%	6.53%	71.04%	71.51%	71.67%	73.59%	50%	4.64kg	72.15%	73.36%	73.89%	75.79%
Lower 25%	4.87%	79.81%	79.39%	80.39%	80.96%	Lower 25%	3.44kg	80.13%	80.34%	80.13%	81.22%

3^rd Bench mark		DT3	RF3	LR3	ANN3	3^rd Bench mark		DT3	RF3	LR3	ANN3

Upper 25%	10.66%	86.00%	90.00%	86.80%	86.31%	Upper 25%	8.02kg	89.20%	90.00%	88.00%	87.51%
50%	8.68%	80.00%	83.20%	84.80%	83.91%	50%	6.22kg	86.00%	85.20%	84.40%	85.23%
Lower 25%	6.68%	84.00%	84.40%	85.60%	86.67%	Lower 25%	4.72kg	87.60%	86.00%	86.40%	86.55%

DT: Decision Tree; RF: Random Forest; LR: Logistic Regression; ANN: Artificial Neural Network

Reduced rate of 1st Bench mark = (initial weight – weight at 2nd prescription)/ initial weight *100

Reduced rate of 2nd Bench mark = (initial weight – weight at 3rd prescription)/ initial weight *100

Reduced rate of 3rd Bench mark = (initial weight – weight at last weight report)/ initial weight *100

Table 3

Features of First and Fourth Quartile based on First Prediction Analysis

	Reduced Rate		Reduced Weight

	More Than Upper 25% (n=6,554)	Less Than Lower 25% (n=6,527)	More Than Upper 25% (n=6,499)	Less Than Lower 25% (n=6,507)
Age (years)	34.92 ± 9.58	37.52 ± 10.59	34.59 ± 9.37	37.84 ± 10.67
Gender (n, %)	Female 5,930 (90)	Female 5,918 (91)	Female 5,400 (83)	Female 6,118 (94)
	Male 624 (10)	Male 609 (9)	Male 1,099 (17)	Male 389 (6)
Height (cm)	162.28 ± 6.67	161.95 ± 6.71	164.22 ± 7.32	160.94 ± 6.26
Weight (kg)	72.76 ± 10.78	72.74 ± 10.5	77.68 ± 12.01	70.06 ± 9.26
BMI (kg/m²)	27.56 ± 3.01	27.68 ± 3.07	28.71 ± 3.23	27.01 ± 2.81
Diet 1 (n, %)	1,908 (29)	1,426 (22)	1,902 (29)	1,393 (21)
Diet 2 (n, %)	2,689 (41)	2,436 (37)	2,593 (40)	2,482 (38)
Diet 3 (n, %)	1,957 (30)	2,665 (41)	2,004 (31)	2,632 (40)
RR (%)	5.89 ± 0.82	1.67 ± 0.79	5.76 ± 0.98	1.71 ± 0.84
RW (kg)	4.29 ± 0.87	1.22 ± 0.61	4.42 ± 0.77	1.18 ± 0.56

Data are expressed as n (%) for categorical variables and mean ± SD for continuous variables.

Diet 1: Weight Loss Experience_None; Diet 2: Diet, exercise only or weight loss drug for less than 3 months; Diet 3: Weight Loss Experience_Weight Loss Drug over 3 Months; RR: Reduced Rate; RW: Reduced Weight

Table 4

Model Performance according to Variables Ranking Based on Feature Importance

	Reduced Rate in 1^st Analysis					Reduced Weight in 1^st Analysis

Ranking	Variables	DT (%)	RF (%)	LR (%)	ANN (%)	Variables	DT (%)	RF (%)	LR (%)	ANN (%)
7						Diet 1	52.19	52.19	52.19	52.44
6						MD_S	54.51	54.51	54.51	54.71
5						Diet 3	54.51	54.51	54.51	54.71
4						Gender	55.07	55.07	55.07	55.22
3	Diet3	52.39	52.39	52.39	53.44	Age	55.91	55.61	55.46	56.48
2	Weight	52.53	52.64	52.39	53.44	BMI	58.61	59.20	58.56	58.90
1	Age	54.06	54.05	53.79	54.69	Weight	58.78	60.06	59.05	59.95

	Reduced Rate in 2^nd Analysis					Reduced Weight in 2^nd Analysis

Ranking	Variables	DT (%)	RF (%)	LR (%)	ANN (%)	Variables	DT (%)	RF (%)	LR (%)	ANN (%)

8						Gender	54.65	54.65	54.65	53.44
7						SAS 1–2_G	57.14	57.14	57.14	57.63
6	SAS1–2 G	54.44	54.44	54.44	55.50	MD_S	57.77	57.77	57.77	58.60
5	SWL1–2 B	56.40	56.40	56.40	56.76	SWL1–2_B	59.73	59.73	59.83	60.01
4	Weight	57.24	56.50	56.50	57.12	Age	58.56	58.67	60.15	61.02
3	Age	55.97	57.03	57.40	59.18	SWL1–2_G	61.31	62.21	62.58	63.52
2	SWL1–2_G	60.94	61.52	62.42	62.21	Weight	64.16	64.64	64.11	66.23
1	RR 1–2	72.04	71.83	70.51	73.52	RR 1–2	73.41	73.15	72.20	75.33

	Reduced Rate in 3^rd Analysis					Reduced Weight in 3^rd Analysis

Ranking	Variables	DT (%)	RF (%)	LR (%)	ANN (%)	Variables	DT (%)	RF (%)	LR (%)	ANN (%)

4	Age	54.80	52.80	53.60	58.95	Age	59.20	59.20	58.00	61.95
3	Weight3	52.80	56.80	50.40	57.63	Weight	59.20	64.80	60.80	67.71
2	RR 1–2	70.80	73.60	69.20	71.55	RW 2–3	71.60	76.80	72.00	76.11
1	RR 1–3	80.00	81.20	81.60	81.88	RW 1–3	86.00	86.00	82.00	83.67

DT: Decision Tree; RF: Random Forest; LR: Logistic Regression; ANN: Artificial Neural Network; Diet 3: Weight Loss Experience_Weight Loss Drug over 3 Months; MD_S: Patients with Medication Dose Change_Stable; Diet 1: Weight Loss Experience_None; SAS 1–2_G: Satiety and Appetite Suppression 1–2_Good; SWL1–2_B: Satisfaction with Weight Loss 1–2_Bad; SWL1–2_G: Satisfaction with Weight Loss 1–2_Good; RR: Reduced Rate; RW: Reduced Weight

Table 5

Prediction Model Performance Results based on AUC

		Algorithm	Sensitivity	Specificity	AUC
1^st Analysis	Reduced Rate	DT	0.557	0.524	0.551
		RF	0.591	0.489	0.557
		LR	0.576	0.499	0.546
		ANN	0.574	0.497	0.550

	Reduced weight	DT	0.709	0.465	0.630
		RF	0.609	0.591	0.640
		LR	0.582	0.602	0.631
		ANN	0.426	0.748	0.620

2^nd Analysis	Reduced Rate	DT	0.722	0.719	0.791
		RF	0.748	0.690	0.789
		LR	0.734	0.677	0.785
		ANN	0.805	0.606	0.785

	Reduced weight	DT	0.784	0.687	0.802
		RF	0.764	0.700	0.816
		LR	0.761	0.685	0.801
		ANN	0.880	0.557	0.798

3^rd Analysis	Reduced Rate	DT	0.762	0.836	0.890
		RF	0.787	0.836	0.890
		LR	0.852	0.781	0.897
		ANN	0.828	0.789	0.880

	Reduced weight	DT	0.873	0.848	0.937
		RF	0.873	0.848	0.939
		LR	0.847	0.795	0.905
		ANN	0.788	0.856	0.920

DT: Decision Tree; RF: Random Forest; LR: Logistic Regression; ANN: Artificial Neural Network; AUC: Area Under the Curve (0.90 – 1.00: excellent, 0.80 – 0.90: good, 0.70 – 0.80: fair, 0.60 – 0.70: poor, 0.50 – 0.60: fail)

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