This document summarizes a study that analyzed mortality risk in patients by using lactate and lactate dehydrogenase (LDH) levels with support vector machines (SVM). The study used data from 686 patient records, including lactate levels and other variables. SVM was used to classify patients and predict survival based on three different variable sets. The best performing model used age, LDH, sex, diagnosis code, and lactate levels, and correctly classified over 98% of patients. The study concludes that analyzing medical data with machine learning can help with treatment planning and assessing patient risk, but requires multidisciplinary expertise and high quality data.

1. Analyzing of Mortality Risk
Yasemin Zeynep ENGİN , Kemal TURHAN,
Aslı YAZAĞAN and Asım ÖREM
IWBBIO 2015
Granada / Spain
Aslı YAZAĞAN
Recep Tayyip Erdogan University
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3. Introduction
Lactate and Lactate dehydrogenase (LDH) are associated with
mortality.
Inpatients were analyzed with Support Vector Machines (SVM)
to identify patients under high risk of death.
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4. Lactate
 Production of cell metabolism
 Arises during respiration
 Accumulated when cells lack of sufficient oxygen
 Plays important role converting glucose to energy
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5. Lactate Dehydrogenase (LDH)
 (LDH) is a biomarker which helps to define:
the cause of tissue damage
the location in the body
monitor the progression of damage
 Many studies that Lactate and LDH are associated with
mortality.
 Increased blood lactate levels in critically ill patients are
associated with increased morbidity and mortality
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6. Support Vector Machine (SVM)
 SVM is a classifier that is highly successful in linear or
nonlinear classification problems.
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7. Support Vector Machine (SVM)
 SVM principle : to determine the optimum decision
boundaries between classes in a decision plane.
 SVM finds a set of datapoints that are the most difficult
training points to classify
 SVM classifier is the most robust and has the best
generalization ability.
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8. Objective of the Study
 Identification of patients in high risk of death.
 Lactate test values of inpatients were analysed with Support
Vector Machines (SVM).
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9. Data
686 patients records with lactate results
 219 patients in pediatric service
 467 patients are adults
 Lactate levels;
 331 patients are normal
 355 patients are high
 89 patients with high lactate levels were recorded as
dead
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10. Methodology
 Survival predictions were made by analyzing lactate test
values ​​of patients with Support Vector Machines (SVM).
3 different variable set are used to run SVM.
Na Test is most frequently requested test with lactate.
The reason of freguently requesting Na and lactade together is
feeding patients through a blood vessels
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Continuous Categorical DataSet
Age, Na Test, LHD, Lactate Level Sex, ICD Code All
Age, LHD Sex, ICD Code, Lactate Level Adults
Age Sex, ICD Code, Lactate Level Adults

11. Results - 1
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Variables Samples Performance
(%)
Lactate, LDH, Sodium (Na), Age, Sex, ICD Code Train
Test
Overall
202
67
269
96,6
97,0
96,6
 54 of 55 alive patients are classified in their true class.
 11 of 12 dead patients are classified in their true class.

12. Results -2
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Variables Samples Performance
(%)
Age, LDH, sex, ICD10, Lactate Train
Test
Overall
192
59
251
99,5
96,6
98,8
 41 of 41 alive patients are classified in their true class.
 16 of 18 dead patients are classified in their true class.

13. Results - 3
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Variables Samples Performance
(%)
Age, sex, ICD10, Lactate Train
Test
Overall
350
117
467
96,5
92,3
95,5
 80 of 84 alive patients are classified in their true class.
 28 of 33 dead patients are classified in their true class.

14. Conclusion
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 This study analyzes patient risk assesments and such results
can be useful while treatments are planning.
 Data mining studies on medical data should be carried out
multidisiplinary. Experts from the fields must work together.
Data mining largely depends on data quality. Better quality
data is needed in medical domain.

15. Thank you
&
Questions?
ayazagan@gmail.com
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