Diabetes Mellitus Forecast

Diabetes Mellitus Forecast

Smith et al. [1] used an early neural network model to forecast the onset of diabetes mellitus. The data were downloaded from ftp://ftp.ncc.up.pt/pub/statlog/. From the 768 samples, an equal number of 170 samples were selected randomly to represent each of the two possible results of diabetes test: positive and negative. The remaining 428 were used as validating samples.

The data set consists of eight input variables:

a: Number of times pregnant;
b: Plasma glucose concentration at 2 hours in an oral glucose tolerance test;
c: Diastolic blood pressure;
d: Triceps skin fold thickness;
e: 2-Hour serum insulin;
f: Body mass index;
g: Diabetes pedigree function; and
h: Age.

Each feature selection process started with eight input nodes, each corresponding to one variable, and two output nodes, corresponding to the two possible test results. Each training session continued for 500 cycles. The process was run for a total of five times. The order in which the inputs were deleted, the average MCSR* and average CASR** at each iteration during the five processes are shown in the table below:

	Iteration	Process
	Iteration	1	2	3	4	5
Inputs used	1	abcdefgh	abcdefgh	abcdefgh	abcdefgh	abcdefgh
	2	abcdefg	abcdefg	abcdefg	abcdefg	abcdefg
	3	abcd fg	abcd fg	abcd fg	abcd fg	abcd fg
	4	ab d fg	ab d fg	ab d fg	ab d fg	ab d fg
	5	ab fg	ab fg	ab fg	ab fg	ab fg
	6	b fg	ab f	ab f	ab f	ab f
	7	b f	b f	b f	b f	b f
	8	b	b	b	b	b
Average MCSR	1	67.143	66.939	67.143	66.735	67.143
	2	66.122	65.714	65.510	65.918	65.918
	3	69.388	70.000	69.592	69.388	69.388
	4	67.143	67.551	68.163	67.347	67.551
	5	73.265	73.469	73.469	73.469	73.469
	6	67.347	68.163	68.163	68.163	68.571
	7	60.204	60.204	60.204	60.204	60.612
	8	60.816	60.816	60.816	61.429	61.429
Average CASR	1	75.047	75.234	74.907	74.860	75.561
	2	74.159	74.252	74.720	74.206	74.299
	3	74.486	74.579	74.486	74.486	74.579
	4	74.019	74.159	74.439	74.206	74.346
	5	73.738	73.645	73.692	73.598	73.598
	6	75.280	74.065	73.972	74.159	73.879
	7	74.299	74.299	74.299	74.299	74.252
	8	72.570	72.570	72.570	72.477	72.477

As inputs were deleted one after another, the MCSR's and CASR's reversed the trend of decline when four and three inputs, respectively, were kept. In particular, the highest MCSR's were reached when four inputs were used. In other words, to achieve the highest success rate for both "positive" and "negative" cases, only four inputs: a, b, f and g, should be used as inputs. The success rate is above 50% even when only one input, b, is used.

[1] J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler and R. S. Johannes, "Using the ADAP learning algorithm to forecast the onset of diabetes mellitus," Proceedings of 12^th Symposium on Computer Applications in Medical Care (R. A. Greenes, Ed.), IEEE Computer Society Press, pp. 261-265, 1988

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*MCSR: The Minimum Class Success Rate was the lowest success rate among all the target classes. The average MCSR is the MCSR's averaged over the five training sessions within each process.

**CASR: The Class Average Success Rate is the success rate averaged over all the target classes. The average CASR is the CASR's averaged over the five training sessions within each process.

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