[Retracted] The Emerging Role of Implementing Machine Learning in Food Recommendation for Chronic Kidney Diseases Using Correlation Analysis

Table 2

Descriptive statistics.

Statistics
		Individuals	Age	Gender	Blood creatinine	Serum albumin	Blood sugar	Potassium	Pus secretion	Bacteria	Risk of CKD	Accuracy of ML
N	Valid	20	20	20	20	20	20	20	20	20	20	20
	Missing	0	0	0	0	0	0	0	0	0	0	0
Mean		10.50	49.50	1.50	2.9420	1.138500000000000	148.50	5.125000000000000	74.250	185.6250	77.50	79.400
Std. error of mean		1.323	1.323	0.115	0.29987	0.100805375274390	3.969	0.315800986632238	1.9843	4.96078	1.323	1.3229
Median		10.50	49.50	1.50	2.8500	1.245000000000000	148.50	5.699999999999999	74.250	185.6250	77.50	79.400
Mode		1^a	40^a	1^a	2.90	0.900000000000000^a	120^a	5.900000000000000	60.0^a	150.00^a	68^a	69.9^a
Std. deviation		5.916	5.916	0.513	1.34104	0.450815343221824	17.748	1.412304946942372	8.8741	22.18530	5.916	5.9161
Variance		35.000	35.000	0.263	1.798	0.203	315.000	1.995	78.750	492.188	35.000	35.000
Skewness		0.000	0.000	0.000	0.561	−0.454	0.000	−1.205	0.000	0.000	0.000	0.000
Std. error of skewness		0.512	0.512	0.512	0.512	0.512	0.512	0.512	0.512	0.512	0.512	0.512
Minimum		1	40	1	1.12	0.220000000000000	120	1.900000000000000	60.0	150.00	68	69.9
Maximum		20	59	2	5.90	2.000000000000000	177	6.700000000000000	88.5	221.25	87	88.9
Percentiles	95	19.95	58.95	2.00	5.8500	1.985000000000000	176.85	6.685000000000000	88.425	221.0625	86.95	88.850

Note. a: multiple modes exist. The smallest value is shown.