Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?

Table 4 Predictive models for predicting mortality among T2DM in-patients seeking health care at the Ho Teaching Hospital

Variables	Model 1		Model 2		Model 3
Variables	aOR(95%CI)	p-value	aOR(95%CI)	p-value	aOR(95%CI)	p-value
Family history
Diabetes	1.17(0.37–3.73)	0.779	1.23(0.38–3.99)	0.728	1.02(0.29–3.56)	0.977
Cardiovascular diseases	0.84(0.28–2.55)	0.761	0.83(0.27–2.51)	0.741	0.90(0.26–3.05)	0.909
Asthma	2.13(0.17–26.84)	0.559	2.38(0.20–28.60)	0.493	3.13(0.22–44.83)	0.436
Lifestyle History
Current Alcoholic			0.60(0.18–1.99)	0.407	0.74(0.21–2.62)	0.612
Current Smoker			2.67(0.58–12.25)	0.207	1.87(0.37–9.50)	0.448
Complications
Cardiovascular diseases					0.79(0.27–2.28)	0.665
Nephropathy					3.83(1.53–9.61)	0.004
Neuropathy					8.51(0.39-187.28)	0.175
Foot Ulcer					0.51(0.05–4.75)	0.553
Diabetic Ketoacidosis					0.64(0.19–2.17)	0.472
Hyperosmolarity coma					1.38(0.14–14.11)	0.784
Hyperosmolarity without coma					1.64(0.26–10.25)	0.600
Pneumonia					1.41(0.11–17.38)	0.788

aOR = Adjusted Odds ratio controlling for sociodemographic variables. P-value significant at < 0.05

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