Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors
Abstract
1. Introduction
- Designed and implemented an AI-enabled mobile system that integrates deep learning techniques to support multiple data collection methods, with a comprehensive system database to facilitate the collection of detailed and diverse patient data.
- Introduced the novel DiabMini dataset, which includes 127 features from 88 diabetic patients, covering personal, medical, and detailed dietary nutrition and lifestyle data. This dataset enables a more holistic and precise analysis of factors affecting diabetes progression.
- Focused on HbA1c as a critical indicator of diabetes progression, we developed a stacking model integrating XGBoost, SVC, ET, and KNN to assess the relationship between various risk factors and HbA1c dynamics, achieving a classification accuracy of 94.23%.
- Applied SHAP to illustrate the contributions of different influencing factors to HbA1c, improving the interpretability of the model’s predictions.
- Supported the advancement of diabetes research by combining continuous and detailed data collection with thorough data analysis, enabling a deeper understanding of diabetes management and progression.
2. Materials and Methods
2.1. Proposed AI-Enabled Mobile System
2.1.1. System Database
2.1.2. User Information Management
2.1.3. Diet Recording Methods
- A.
- Diet Recording by Voice Input
- B.
- Diet Recording by Single-Shot Cuisine Photography
2.2. The Smartphone Application for Data Recording and Analysis
2.3. Diabetes Progression Analysis
2.3.1. DiabMini Dataset
- Personal information (n = 6): Including basic information such as height and weight;
- Medical information (n = 32): Participants underwent two medical examinations, one before and one after the project, recording 16 test indicators through routine blood tests and body composition analysis. Both examinations were conducted by professional hospitals using consistent instruments and procedures;
- Lifestyle (n = 10): Lifestyle data were collected through an online questionnaire in the application. It contained ten questions about activities and habits, such as exercise frequency and sleep duration. These questions were designed based on physician expertise and diabetes risk factors;
- Nutrient intake (n = 79): Between two medical examinations, all participants were required to record their complete daily dietary intake for 14 days, including meals, beverages, snacks, and fruits. These records were automatically converted into the intake of 79 specific nutrients. The convenient recording methods and extensive food-nutrient database of the application ensured efficient dietary tracking.The detailed annotation of all data is presented in Appendix A.
2.3.2. Model Establishment and Evaluation
3. Results
3.1. HbA1c Classification Experimental Results
3.2. HbA1c Classification Model Interpretability
4. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
- -
- Age— age, years
- -
- Gender—(1—Female, 2—male)
- -
- Chronic disease—(1—Heart disease, 2—Diabetes, 3—No, 4—Hypertension)
- -
- Height—height, centimeter
- -
- Weight—weight, kilogram
- -
- BMI—Body Mass Index
- -
- BFP: Body fat percentage, the proportion of total body fat to body weight
- -
- WHR: Waist to hip ratio, the ratio of waist circumference to hip circumference
- -
- VFA: Visceral fat area, refers to the tomographic area index of visceral fat in CT imaging
- -
- HbA1c: Glycosylated hemoglobin, the compounds that bind glucose and hemoglobin
- -
- FBG: Fasting blood glucose, the blood glucose measured before breakfast the next morning for more than eight to twelve hours of fasting overnight
- -
- TP: Total protein, the general name of albumin and globulin
- -
- Alb: Albumin, the most important protein in human plasma
- -
- TG: Serum triglycerides, the important component of blood lipids, mmol/L
- -
- TC: Serum total cholesterol, the sum of cholesterol contained in all lipoproteins in the blood, mmol/L
- -
- HDL: High-density lipoprotein, one of the serum proteins
- -
- LDL: Low-density lipoprotein, one of the lipoprotein components in blood lipids
- -
- AI: Arteriosclerosis index, the index to evaluate the degree of arteriosclerosis
- -
- WBC: White blood cell, the cells with motility and phagocytosis
- -
- RBC: Red blood cells, the most numerous types of blood cells in the blood
- -
- HGB: Hemoglobin, the protein contained in red blood cells
- -
- LY: Lymphocyte count, to count lymphocytes and calculate the percentage
- -
- Working hours—(1—8–12 h, 2—4–8 h, 3—Less than 4 h, 4— More than 12 h)
- -
- Work intensity—(1—Student, 2—Office work, 3—Retire, 4—High intensity)
- -
- Exercise weekly—(1—3–4 times, 2—1–2 times, 3—Occasionally or hardly, 4—More than 5 times)
- -
- Sleep time—(1—4–6 h, 2—6–8 h, 3—More than 8 h, 4—Less than 4 h)
- -
- Meal habits—(1—No breakfast, 2—Very irregular, 3—Three meals are regular)
- -
- Food selection—(1—Pay attention to nutritional value, 2—Personal preference, 3—Careful selection based on health condition)
- -
- Dietary preferences—(1—Vegetarian diet, 2—Balanced diet, 3—Meat diet)
- -
- Water intake daily—(1—1000–2000 mL, 2—2000–3000 mL, 3—More than 3000 mL, 4—0–1000 mL)
- -
- Drinking weekly—(1—3–4 times, 2—Less than 3 times, 3—Never or rarely, 4—More than 5 times)
- -
- Smoking daily—(1—5–10 cigarettes, 2—1–5 cigarettes, 3—No smoking, 4—More than 10 cigarettes)
- -
- Cholesterol: the component of lipids, mg
- -
- Purine: the organic compounds produced by the body’s metabolism, mg
- -
- Energy: kilocalorie, kcal
- -
- Protein: g
- -
- Fat: g
- -
- Carbohydrate: g
- -
- Water: g
- -
- Dietary fiber: g
- -
- Total vitamin A: g
- -
- Vitamin E: mg
- -
- Vitamin B1: mg
- -
- Vitamin B2: mg
- -
- Vitamin B3: mg
- -
- Vitamin C: mg
- -
- Ca: Calcium, mg
- -
- P: Phosphorus, mg
- -
- K: Potassium, mg
- -
- Na: Sodium, mg
- -
- Mg: Magnesium, mg
- -
- Fe: Iron, mg
- -
- Zn: Zinc, mg
- -
- Se: Selenium, g
- -
- Cu: Copper, mg
- -
- Mn: Manganese, mg
- -
- Total fatty acids: g
- -
- SFA: Saturated fatty acids, g
- -
- MUFA: Monounsaturated fatty acid, g
- -
- PUFA: Polyunsaturated fatty acid, g
- -
- Hexanoic (Caproic), mg
- -
- Octanoic (Caprylic), mg
- -
- Decanoic (Capric), mg
- -
- Henedecanoic (Undecylic), mg
- -
- Dodecanoic (Lauric), mg
- -
- Tridecanoic (Tridecylic), mg
- -
- Tetradecanoic (Myristic), mg
- -
- Pentadecanoic (Pentadecylic), mg
- -
- Hexadecanoic (Palmitic), mg
- -
- Heptadecanoic (Margaric), mg
- -
- Nonadecanoic (Nondecylic), mg
- -
- Eicosanoic (Arachidic), mg
- -
- Docosanoic (Behenic), mg
- -
- C14:1 (n-5): cis-9-Tetradecenoic, Myristoleic, mg
- -
- C15:1 (n-5): 10-Pentadecenoic, mg
- -
- C16:1 (n-7): cis-9-Hexadecenoic, Palmitoleic, mg
- -
- C17:1 (n-7): 10-Heptadecenoic, mg
- -
- C18:1 (n-9): cis-9-Octadecenoic, Oleic, mg
- -
- C20:1 (n-11): cis-9-Eicosenoic, Gadoleic, mg
- -
- C22:1 (n-13): cis-9-Docosenoic, mg
- -
- C16:2 (n-4): cis, cis-9,12-Hexadecadienoic, mg
- -
- C18:2 (n-6): cis, cis-9,12-Octadecadienoic, Linoleic, mg
- -
- C18:3 (n-3): all cis-9,12,15-Octadecatrienoic, -Linolenic, mg
- -
- C20:2 (n-6): cis, cis-11,14-Eicosadienoic, mg
- -
- C20:3 (n-9): all cis-5,8,11-Eicosatrienoic, Mead, mg
- -
- C20:4 (n-6): all cis-5,8,11,14-Eicosatetraenoic, Arachidonic, mg
- -
- C20:5 (n-3): all cis-5,8,11,14,17-Eicosapentaenoic, mg
- -
- C22:3 (n-3): all cis-13,16,19-Docosatrienoic, mg
- -
- C22:4 (n-6): all cis-7,10,13,16-Docosatetraenoic, mg
- -
- C22:5 (n-3): all cis-7,10,13,16,19-Docosapentaenoic, mg
- -
- C22:6 (n-3): all cis-4,7,10,13,16,19-Docosahexaenoic, mg
- -
- Isoleucine, mg
- -
- Leucine, mg
- -
- Lysine, mg
- -
- TSAA: Total sulfur containing amino acids, mg
- -
- Methionine, mg
- -
- Cystine, mg
- -
- TAAA: Total aromatic amino acids, mg
- -
- Phenylalanine, mg
- -
- Tyrosine, mg
- -
- Threonine, mg
- -
- Tryptophane, mg
- -
- Valine, mg
- -
- Arginine, mg
- -
- Histidine, mg
- -
- Alanine, mg
- -
- Aspartic acid, mg
- -
- Glutamic acid, mg
- -
- Glycine, mg
- -
- Proline, mg
- -
- Serine, mg
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Ingredient | Energy | Fat | Carbohydrate | Protein | Water | Dietary Fiber | Vitamin B1 | Ca | Fe | Na | ... |
---|---|---|---|---|---|---|---|---|---|---|---|
Rice | 116 kcal | g | g | g | g | g | mg | 7 mg | mg | mg | |
Potato | 77 kcal | g | g | 2 g | g | g | mg | 8 mg | mg | mg | |
Pork | 395 kcal | 37 g | g | g | g | - * | mg | 6 mg | mg | mg | |
Chicken | 167 kcal | g | g | g | 69 g | - * | mg | 9 mg | mg | mg | |
Mushroom | 24 kcal | g | g | g | g | g | mg | 6 mg | mg | mg | |
Tomato | 20 kcal | g | 4 g | g | g | g | mg | 10 mg | mg | 5 mg | |
... | |||||||||||
Packaged Foods | Energy | Fat | Carbohydrate | Protein | Na | ||||||
Oatmeal | 377 kcal | g | g | 15 g | mg | ||||||
Biscuit | 435 kcal | g | g | 9 g | mg | ||||||
Fried Chips | 615 kcal | g | g | 4 g | mg | ||||||
Spicy Kelp | kcal | g | g | g | 2590 mg | ||||||
Cheese | 328 kcal | g | g | g | 584 mg | ||||||
... | |||||||||||
Dishes | Ingredient | Amount | Ingredient | Amount | Ingredient | Amount | Ingredient | Amount | Ingredient | Amount | ... |
Spaghetti with Sauce | Macaroni | 300 g | Pork | 100 g | Tomatoes | 100 g | Onion | 50 g | Pepper | 3 g | |
Sandwich | Bread | 100 g | Luncheon Meat | 80 g | Cucumber | 50 g | Tomato | 50 g | Lettuce | 30 g | |
Yam Sparerib Porridge | Yam | 150 g | Pork Chop | 150 g | Rice | 150 g | Water | 400 g | Coriander | 10 g | |
Meat Floss Sushi | Rice | 200 g | Pork Floss | 50 g | Vinegar | 5 g | Laver | 3 g | Cucumber | 30 g | |
Roast Chicken | Rice | 250 g | Chicken | 200 g | Cucumber | 80 g | Chinese Onion | 10 g | Ginger | 10 g | |
... |
Base Model | Meta Model | Evaluation Metrics (%) | |||||
---|---|---|---|---|---|---|---|
XGBoost | SVC | ET | KNN | Accuracy | Macro-Precision | Macro-Recall | Macro-F1 |
✓ | - | - | - | 80.18 ± 6.66 | 82.70 ± 4.68 | 80.42 ± 6.33 | 79.91 ± 7.09 |
- | ✓ | - | - | 86.51 ± 3.24 | 87.69 ± 3.67 | 86.55 ± 3.58 | 85.69 ± 3.82 |
- | - | ✓ | - | 88.45 ± 3.33 | 89.40 ± 2.98 | 88.42 ± 3.42 | 88.46 ± 3.24 |
- | - | - | ✓ | 73.04 ± 2.83 | 80.29 ± 2.10 | 73.15 ± 3.05 | 68.64 ± 3.95 |
✓ | ✓ | - | ✓ | 89.11 ± 2.52 | 91.16 ± 1.84 | 89.15 ± 2.73 | 88.59 ± 2.92 |
✓ | - | ✓ | ✓ | 93.59 ± 2.04 | 94.29 ± 1.76 | 93.64 ± 1.84 | 93.42 ± 2.08 |
- | ✓ | ✓ | ✓ | 92.34 ± 3.16 | 93.36 ± 2.65 | 92.48 ± 2.94 | 92.12 ± 3.40 |
✓ | ✓ | ✓ | ✓ | 94.23 ± 1.27 | 94.97 ± 1.07 | 94.24 ± 1.24 | 94.16 ± 1.26 |
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Jiang, H.; Wang, H.; Pan, T.; Liu, Y.; Jing, P.; Liu, Y. Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors. Bioengineering 2024, 11, 1053. https://doi.org/10.3390/bioengineering11111053
Jiang H, Wang H, Pan T, Liu Y, Jing P, Liu Y. Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors. Bioengineering. 2024; 11(11):1053. https://doi.org/10.3390/bioengineering11111053
Chicago/Turabian StyleJiang, Huaiyan, Han Wang, Ting Pan, Yuhang Liu, Peiguang Jing, and Yu Liu. 2024. "Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors" Bioengineering 11, no. 11: 1053. https://doi.org/10.3390/bioengineering11111053
APA StyleJiang, H., Wang, H., Pan, T., Liu, Y., Jing, P., & Liu, Y. (2024). Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors. Bioengineering, 11(11), 1053. https://doi.org/10.3390/bioengineering11111053