Enhancing Diabetes Prediction and Prevention through Mahalanobis Distance and Machine Learning Integration
Abstract
:1. Introduction
2. Related Works
3. Methodology
3.1. Preparing Experimental Dataset
3.2. Feature Selection Based on MC Analysis
3.3. Feature Selection Based on Reliability Analysis
3.4. Outlier Detection Based on MAH
3.5. Classifiers
3.6. Evaluation Metrics
4. Experimental Study
4.1. Classifier Results
4.2. Hyperparameter Results
5. Discussions
Limitations and Future Work
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Diabetes | Total | ||||||||
---|---|---|---|---|---|---|---|---|---|
Before COVID-19 Pandemic | During COVID-19 Pandemic | ||||||||
2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | |||
Residential area | |||||||||
Rural | 76 (0.7) | 164 (1.4) | 185 (1.6) | 57 (0.5) | 247 (2.2) | 244 (2.1) | 244 (2.1) | 1217 (10.7) | |
Urban | 158 (1.4) | 144 (1.3) | 154 (1.4) | 51 (0.4) | 171 (1.5) | 181 (1.6) | 180 (1.6) | 1039 (9.1) | |
Sex | |||||||||
Male | 123 (1.3) | 159 (1.7) | 184 (2.0) | 0 (0) | 222 (2.4) | 218 (2.4) | 216 (2.3) | 1122 (12.2) | |
Female | 111 (0.8) | 149 (1.1) | 155 (1.1) | 108 (0.8) | 196 (1.4) | 207 (1.5) | 208 (1.5) | 1134 (8.4) | |
Age | |||||||||
19–29 years old | 0 (0.0) | 1 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 2 (0.1) | 1 (0.0) | 4 (0.2) | |
30–39 years old | 2 (0.1) | 6 (0.2) | 7 (0.2) | 3 (0.1) | 11 (0.3) | 10 (0.3) | 3 (0.1) | 42 (10.3) | |
40–49 years old | 19 (0.4) | 20 (0.5) | 28 (0.7) | 8 (0.2) | 37 (0.9) | 34 (0.8) | 30 (0.7) | 176 (40.1) | |
50–59 years old | 43 (0.9) | 60 (10.3) | 79 (10.7) | 24 (0.5) | 71 (10.5) | 107 (20.3) | 63 (10.4) | 447 (90.7) | |
60–69 years old | 96 (20.2) | 10 (20.3) | 106 (20.4) | 32 (0.7) | 127 (20.9) | 137 (30.2) | 154 (30.6) | 752 (170.4) | |
70–79 years old | 67 (20.3) | 109 (30.8) | 97 (30.4) | 33 (10.2) | 142 (50.0) | 107 (30.7) | 138 (40.8) | 693 (240.2) | |
Over 80 years old | 7 (10.0) | 12 (10.7) | 22 (30.0) | 8 (10.1) | 30 (40.1) | 28 (30.9) | 35 (40.8) | 142 (190.5) | |
Hypertension | |||||||||
Normal | 49 (0.5) | 45 (0.4) | 60 (0.6) | 17 (0.2) | 64 (0.6) | 85 (0.8) | 77 (0.7) | 397 (3.8) | |
Pre-hypertension | 49 (0.9) | 69 (1.2) | 57 (1.0) | 15 (0.3) | 91 (1.6) | 85 (1.5) | 79 (1.4) | 445 (7.9) | |
Hypertension | 136 (2.0) | 194 (2.9) | 222 (3.3) | 76 (1.1) | 263 (3.9) | 255 (3.8) | 268 (3.9) | 1414 (20.8) | |
Obesity | |||||||||
Underweight | 1 (0.1) | 2 (0.2) | 4 (0.4) | 0 (0.0) | 3 (0.3) | 2 (0.2) | 7 (0.8) | 19 (2.1) | |
Normal | 127 (1.2) | 152 (1.5) | 88 (0.8) | 32 (0.3) | 92 (0.9) | 87 (0.8) | 112 (1.1) | 690 (6.6) | |
Pre-obesity stage | 106 (1.8) | 154 (2.6) | 80 (1.4) | 30 (0.5) | 102 (1.7) | 111 (1.9) | 93 (1.6) | 676 (11.4) | |
1st stage obesity | - | - | 144 (3.1) | 38 (0.8) | 182 (3.9) | 171 (3.6) | 181 (3.8) | 716 (15.2) | |
2nd stage obesity | - | - | 22 (3.2) | 7 (1.0) | 35 (5.1) | 52 (7.6) | 21 (3.1) | 137 (20.0) | |
3rd stage obesity | - | - | 1 (1.1) | 1 (1.1) | 4 (4.3) | 2 (2.2) | 10 (10.8) | 18 (19.4) |
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Authors | Years | Algorithms | Comments |
---|---|---|---|
[25] | 2024 | SECNN | The SECNN model has an accuracy of 94.12% in NHANES dataset and an accuracy of 89.47% on the PIMA Indian dataset. |
[26] | 2023 | CBC | CatBoost classifier best performance, with an accuracy of 85% and an AUC of 93%. |
[27] | 2023 | DDLNN | The model is best, with an accuracy of 84.42%. |
[31] | 2023 | PCA + ELM | PCA has the best performance with an accuracy of 90.57%. |
[29] | 2020 | AB + XB | The best model reaches an AUC of 95%. |
[30] | 2023 | RFWBP | The best performance, with an accuracy of 95.83%. |
Feature | Descriptions | Mean | Std. Dev | p-Value | Tolerance | VIF 1 |
---|---|---|---|---|---|---|
BP6_31 | Attempted suicide for 1 year | 2 | 0.04 | 0.189 | 0.193 | 5.171 |
BP7 | Counseling for mental issues for 1 year | 1.98 | 0.143 | 0.149 | 0.235 | 4.261 |
HE_HbA1c | Glycated hemoglobin | 5.69 | 0.623 | <0.001 | 0.282 | 3.546 |
HE_glu | Fasting blood sugar | 99.12 | 16.855 | <0.001 | 0.315 | 3.173 |
HE_sbp | End systolic blood pressure | 118.67 | 16.223 | 0.290 | 0.350 | 2.857 |
HE_alt | Serum glutamic pyruvic transaminase | 21.18 | 13.468 | <0.001 | 0.380 | 2.632 |
HE_HP | Hypertension | 1.86 | 0.859 | <0.001 | 0.392 | 2.554 |
HE_ast | Serum glutamic oxaloacetic transaminase | 22.8 | 8.248 | 0.670 | 0.428 | 2.335 |
Age | Age | 4.73 | 1.686 | <0.001 | 0.441 | 2.270 |
Sex | Sex | 1.6 | 0.489 | <0.001 | 0.442 | 2.260 |
HE_obe | Obesity (over 19 years old) | 2.75 | 0.964 | <0.001 | 0.464 | 2.155 |
HE_dbp | End diastolic blood pressure | 75.03 | 9.623 | <0.001 | 0.484 | 2.065 |
BO1 | Subjective body shape recognition | 3.37 | 0.906 | <0.001 | 0.487 | 2.053 |
HE_HCT | Hematocrit | 42.33 | 4.201 | <0.001 | 0.520 | 1.924 |
BS13 | Exposure to secondhand smoke indoors in public | 1.85 | 0.36 | 0.101 | 0.526 | 1.900 |
HE_crea | Blood creatinine | 0.8 | 0.183 | 0.686 | 0.592 | 1.688 |
HE_BUN | Blood urea nitrogen | 14.6 | 4.285 | <0.001 | 0.640 | 1.563 |
BS9_2 | Exposure to secondhand smoke indoors at home | 2.8 | 0.498 | 0.294 | 0.712 | 1.405 |
BP6_2 | Planned suicide for a year | 1.99 | 0.101 | <0.004 | 0.759 | 1.317 |
BP1 | Perceived level of stress on a daily basis | 2.88 | 0.72 | 0.227 | 0.789 | 1.268 |
BO2_1 | Weight control for 1 year | 2.28 | 1.3 | 0.213 | 0.805 | 1.242 |
Year | Survey year | 2018.12 | 2.032 | <0.001 | 0.827 | 1.209 |
HE_HCHOL | Hypercholesterolemia | 0.24 | 0.428 | <0.001 | 0.851 | 1.175 |
HE_WBC | Leukocyte | 6.09 | 1.628 | <0.001 | 0.857 | 1.166 |
HE_HTG | Hypertriglyceridemia | 0.12 | 0.328 | <0.005 | 0.888 | 1.126 |
Graduate | Education level—graduation status | 1.56 | 1.363 | 0.319 | 0.896 | 1.116 |
DJ8_pt | Allergic rhinitis treatment | 6.91 | 2.699 | 0.362 | 0.907 | 1.102 |
DJ6_pt | Sinusitis treatment | 7.54 | 1.847 | 0.261 | 0.948 | 1.055 |
BD1_11 | Frequency of drinking per year | 3.69 | 2.122 | <0.019 | 0.951 | 1.051 |
DL1_pt | Atopic dermatitis treatment | 7.88 | 0.968 | 0.176 | 0.958 | 1.044 |
HE_fh | Family history of chronic dis. diagnosed | 0.67 | 0.793 | 0.060 | 0.972 | 1.028 |
DH4_pt | Otitis media treatment | 7.63 | 1.676 | 0.245 | 0.975 | 1.026 |
Region | Region | 7.39 | 4.918 | 0.520 | 0.976 | 1.024 |
HE_hepaB | Hepatitis B surface antigen positive | 0.02 | 0.129 | <0.043 | 0.991 | 1.009 |
Class | Dataset with FS | Dataset with FS and MAH | ||||
---|---|---|---|---|---|---|
Total | Train 70% | Test 30% | Total | Train 70% | Test 30% | |
Normal | 14,648 | 10,186 | 4442 | 13,318 | 9324 | 3994 |
Pre-diabetes | 8876 | 6238 | 2638 | 7775 | 5426 | 2349 |
Diabetes | 3778 | 2673 | 1105 | 2518 | 1777 | 741 |
Total | 27,282 | 19,097 | 8185 | 23,611 | 16,527 | 7084 |
Total Case | Cronbach’s Alpha | Features Number | |
---|---|---|---|
Dataset without MAH * | 27,282 | 0.599 | 24 |
Dataset with MAH ** | 23,611 | 0.641 | 24 |
Algorithms (Selected Features) | Accuracy (%) | AUC (%) | F1-Score (%) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Base (165) | MC (24) | XGB (66) | RF (22) | Base (165) | MC (24) | XGB (66) | RF (22) | Base (165) | MC (24) | XGB (66) | RF (22) | ||
Baseline model * | XGB | 96.78 | 97.75 | 97.72 | 97.74 | 97.83 | 98.48 | 98.47 | 98.46 | 97.24 | 97.95 | 97.96 | 97.97 |
KNN | 40.09 | 77.47 | 66.13 | 66.38 | 58.50 | 83.56 | 75.74 | 75.92 | 44.44 | 79.71 | 70.68 | 70.83 | |
DT | 93.81 | 94.07 | 93.96 | 93.93 | 96.92 | 97.05 | 97.04 | 96.94 | 95.87 | 96.17 | 95.92 | 96.05 | |
NB | 48.21 | 53.33 | 48.21 | 53.14 | 78.23 | 84.29 | 79.37 | 83.43 | 63.75 | 70.40 | 64.29 | 69.26 | |
RF | 95.42 | 97.26 | 96.91 | 97.25 | 97.29 | 97.26 | 98.04 | 98.21 | 97.49 | 97.74 | 97.68 | 97.91 | |
Proposed model ** | MAH_XGB | 97.79 | 97.98 | 97.92 | 97.76 | 98.52 | 98.64 | 98.61 | 98.47 | 97.99 | 98.21 | 98.17 | 97.94 |
MAH_KNN | 41.93 | 78.71 | 67.69 | 67.48 | 59.53 | 84.39 | 76.76 | 76.59 | 44.12 | 79.71 | 70.81 | 70.98 | |
MAH_DT | 94.51 | 95.31 | 95.05 | 94.13 | 97.17 | 97.58 | 97.46 | 97.03 | 96.34 | 96.86 | 96.63 | 96.04 | |
MAH_NB | 54.94 | 60.92 | 55.71 | 64.60 | 79.45 | 85.27 | 80.47 | 83.81 | 66.12 | 71.96 | 67.68 | 72.65 | |
MAH_RF | 96.67 | 97.62 | 97.29 | 97.25 | 97.96 | 98.43 | 98.28 | 98.22 | 97.51 | 97.93 | 97.94 | 97.74 |
Algorithms (Selected Features) | Accuracy (%) | AUC (%) | F1-Score (%) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Base (165) | MC (24) | XGB (66) | RF (22) | Base (165) | MC (24) | XGB (66) | RF (22) | Base (165) | MC (24) | XGB (66) | RF (22) | ||
Baseline model * | XGB | 97.02 | 97.96 | 97.83 | 97.77 | 97.51 | 98.58 | 98.55 | 98.5 | 97.04 | 98.08 | 98.03 | 97.96 |
KNN | 33.68 | 77.6 | 65.66 | 66.01 | 54.00 | 83.62 | 75.35 | 75.64 | 33.93 | 77.93 | 66.76 | 67.12 | |
DT | 94.501 | 94.44 | 94.39 | 94.63 | 97.5 | 97.2 | 97.15 | 97.3 | 96.69 | 96.30 | 96.24 | 96.41 | |
NB | 42.08 | 54.00 | 48.56 | 53.9 | 77.77 | 84.66 | 79.8 | 83.78 | 69.65 | 78.54 | 72.73 | 77.35 | |
RF | 96.08 | 97.55 | 97.178 | 97.47 | 96.68 | 98.36 | 98.21 | 98.35 | 96.72 | 97.80 | 97.63 | 97.80 | |
Proposed model ** | MAH_XGB | 97.22 | 98.04 | 97.96 | 97.69 | 97.44 | 98.71 | 98.72 | 98.39 | 97.22 | 98.24 | 98.27 | 97.81 |
MAH_KNN | 48.52 | 78.80 | 67.36 | 67.4 | 64.69 | 84.38 | 76.46 | 76.47 | 52.67 | 78.79 | 68.02 | 67.97 | |
MAH_DT | 94.14 | 94.97 | 94.94 | 94.16 | 95.07 | 97.47 | 97.49 | 97.08 | 94.75 | 94.68 | 96.61 | 96.16 | |
MAH_NB | 53.60 | 61.01 | 57.03 | 65.61 | 82.81 | 85.23 | 81.51 | 84.44 | 77.4 | 79.16 | 74.94 | 78.44 | |
MAH_RF | 96.85 | 97.71 | 97.28 | 97.17 | 96.73 | 98.44 | 98.34 | 98.16 | 96.74 | 97.89 | 97.82 | 97.52 |
Algorithms | Accuracy (%) | p-Value | 95% CI |
---|---|---|---|
MAH_XGB | 98.04 | 3.52 × 10−15 | 97.89~98.59 |
MAH_KNN | 78.80 | 3.89 × 10−15 | 77.66~79.89 |
MAH_DT | 94.97 | 3.33 × 10−15 | 93.56~97.52 |
MAH_RF | 97.71 | 3.98 × 10−15 | 97.62~98.08 |
MAH_NB | 61.01 | 3.93 × 10−15 | 79.88~81.96 |
Algorithms | Accuracy (%) | AUC (%) |
---|---|---|
CatBoost CBC [26] | 85.0 | 93.0 |
DDLNN [27] | 84.42 | - |
AB + XB [29] | - | 95.0 |
RFWBP [30] | 83.0 | 86.0 |
PCA + ELM [31] | 90.57 | - |
Proposed method_MAH_XGB | 98.04 | 98.71 |
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Dashdondov, K.; Lee, S.; Erdenebat, M.-U. Enhancing Diabetes Prediction and Prevention through Mahalanobis Distance and Machine Learning Integration. Appl. Sci. 2024, 14, 7480. https://doi.org/10.3390/app14177480
Dashdondov K, Lee S, Erdenebat M-U. Enhancing Diabetes Prediction and Prevention through Mahalanobis Distance and Machine Learning Integration. Applied Sciences. 2024; 14(17):7480. https://doi.org/10.3390/app14177480
Chicago/Turabian StyleDashdondov, Khongorzul, Suehyun Lee, and Munkh-Uchral Erdenebat. 2024. "Enhancing Diabetes Prediction and Prevention through Mahalanobis Distance and Machine Learning Integration" Applied Sciences 14, no. 17: 7480. https://doi.org/10.3390/app14177480