Annals of translational medicine · 2021
Bone metabolic biomarker-based diagnosis of type 2 diabetes osteoporosis by support vector machine.
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Wang Chuan, Zhang Taomin, Wang Peng, Liu Xuan, Zheng Liming, Miao Lei, Zhou Deyu, Zhang Yibo, Hu Yezi, Yin Han, Jiang Qing, Jin Hui, Sun Jianfei
Аннотация
BACKGROUND: Diabetes has significant effects on bone metabolism. Both type 1 and type 2 diabetes can cause osteoporotic fracture. However, it remains challenging to diagnose osteoporosis in type 2 diabetes by bone mineral density which lacks regular changes. Seen another way, osteoporosis can be ascribed to the imbalance of bone metabolism, which is closely related to diabetes as well.
METHODS: Here, to assist clinicians in diagnosing osteoporosis in type 2 diabetes, an efficient and simple SVM (support vector machine) model was established based on different combinations of biochemical indexes, which were collected from patients who did the test of bone turn-over markers (BTMs) from January 2016 to March 2018 in the department of endocrine, Zhongda Hospital affiliated to Southeast University. The classification was done based on a software package of machine learning in Python. The classification performance was measured by SKLearn program incorporated in the Python software package and compared with the clinical diagnostic results.
RESULTS: The predicting accuracy rate of final model was above 88%, with feature combination of sex, age, BMI (body mass index), TP1NP (total procollagen I N-terminal propeptide) and OSTEOC (osteocalcin).
CONCLUSIONS: Experimental results show that the model showed an anticipant result for early detection and daily monitoring on type 2 diabetic osteoporosis.
METHODS: Here, to assist clinicians in diagnosing osteoporosis in type 2 diabetes, an efficient and simple SVM (support vector machine) model was established based on different combinations of biochemical indexes, which were collected from patients who did the test of bone turn-over markers (BTMs) from January 2016 to March 2018 in the department of endocrine, Zhongda Hospital affiliated to Southeast University. The classification was done based on a software package of machine learning in Python. The classification performance was measured by SKLearn program incorporated in the Python software package and compared with the clinical diagnostic results.
RESULTS: The predicting accuracy rate of final model was above 88%, with feature combination of sex, age, BMI (body mass index), TP1NP (total procollagen I N-terminal propeptide) and OSTEOC (osteocalcin).
CONCLUSIONS: Experimental results show that the model showed an anticipant result for early detection and daily monitoring on type 2 diabetic osteoporosis.