Analysis of Death Causes in the STULONG Data Set

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Analysis of Death Causes in the STULONG Data Set

• Jan Burian, Jan Rauch
• EuroMISE Cardio
• University of Economics Prague

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Analytic questions

• Are there strong relations concerning death
  cause?
• General characteristics (?) ? Death cause (?)
• Examinations (?) ? Death cause (?)
• Vices(?) ? Death cause (?)
• Combinations (?) ? Death cause (?)

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Example of relation founded implication

• A Cholesterollt250273gt Coffee(3 and more cups)
• ? 0.6315 Death
  cause (tumorous disease) S

63 of patients satisfying A satisfy also S there
are 15 patients satisfying both A and S
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Example of relation above average

• A Age( ?65) ?0.7615 Death cause (general
  atherosclerosis) S
• A Age( ?65) ?0.115 Death cause (general
  atherosclerosis) S

relative frequency of S 22/389 0.057
relative frequency of S if A 15/151
0.099 relative frequency of S if A is 76 per
cent higher than the relative frequency of
S there are 15 patients satisfying both A and S
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Example of task
Vices(?) ?0.5515 Death cause (?) For which
combinations of vices is relative frequency of
some death causes at least 55 per cent higher
than relative frequency of the same death cause
among all patients ? We require at least 15
patients with particular death cause satisfying
both particular condition.

• Liquors(?) Smoking(?) ?0.5515 Death cause(?)
• Alcohol(?) Tea(?) ?0.5515 Death cause(?)
• Beer 12(?) Wine(?) ?0.5515 Death cause(?)
• Liquors(?) Smoking(?) Coffee(?) Beer 12(?)
  ?0.5515 Death cause(?)
• ????? ?0.5515 Death cause(?)

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4ft-Miner application Vices(?) ?0.5515 Death
cause (?)
?0.7515
Death cause(?)
Vices(?) Antecedent
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Dealing with attributes

• An example Age

Predefined intervals length 10 Agelt40,50),
Agelt50,60), , Age lt70,80) Predefined intervals
length 5 Agelt40,45), Agelt45,50), Age
lt70,75) Sliding window length 10 Sliding window
length 5 Sliding window length 2
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Sliding window length 5
44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
....., 67, 68, 69, 70, 71, 72, 73, 74 44, 45, 46,
47, 48, 49, 50, 51, 52, 53, 54, ....., 67, 68,
69, 70, 71, 72, 73, 74 44, 45, 46, 47, 48, 49,
50, 51, 52, 53, 54, ....., 67, 68, 69, 70, 71,
72, 73, 74 ........... 44, 45, 46, 47, 48, 49,
50, 51, 52, 53, 54, ....., 67, 68, 69, 70, 71,
72, 73, 74 44, 45, 46, 47, 48, 49, 50, 51, 52,
53, 54, ....., 67, 68, 69, 70, 71, 72, 73, 74
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Dealing with attributes

• An other example Marital status

81.5
1.3
10.0
7.2

Marital status(divorced) 39 patients Marital
status(single) 28 patients
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Dealing with attributes

• Some further examples
• Predefined intervals, sliding windows
• Cholesterol
• Subscapular
• Height,
• Weight,
• Particular values
• Activity after job
• Physical activity in a job
• Education
• Transport
• Responsibility

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4ft-Miner result example
Beer 12(yes) Vine(yes) ?0.5515 Death cause
(tumorous disease)
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Tasks Antecedent ? Death cause (?)
Solution time in all cases 8 sec Intel
Pentium on 3Ghz, 512 MB RAM
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Conclusions

• Only 389 patients with death code
• Some potentially interesting rules
• Fast work with 4ft-Miner
• Possibility of tuning work with attributes
• predefined intervals,
• sliding windows