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Preventing Lyme Diease

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by

aibesse tessema

on 6 May 2015

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Transcript of Preventing Lyme Diease

Preventing Lyme Disease
A Data Driven approach for Targeting Prevention Treatment
Data Cleaning Efforts
1. Data Source- Vet Hospital Software
2. Data Cleaning Efforts
Transformed Variables
Removed Empty Records
3. Splitting Variable

Data Exploration:
Distribution based on Lyme Disease diagnosis and Prevention Treatment
Data Cleaning Efforts
Box Plot distribution
Agenda
1. Project Objective
2. Data Cleaning and Exploration
Process
3. The Model and Findings
4. Conclusion
Determine the characteristics of dogs that have not received
tick prevention or Lyme vaccine
Data Mining Task:
Prediction
Model:
Classification Tree
Association rules
Objective:
Total Record: 1517 dogs
Lyme Disease: 55 dogs
No Prevention: 874
Training data = 934
Validation data = 583
Independent:
Age,
# dogs in household,
# years being seen at hospital,
dog size,
gender,
neutered status,
geographical region

Dependent:
Not on tick prevention or
Lyme vaccine
Variables Used
Findings
Classification Tree Model:
Classification Cost:
Dogs that visited the hospital for
< than 1.2 yrs

OR

Visited the hospital for > than 1.2 yrs and have only 1 dog in the same household
Then Dog Belongs to the Success class!!
Misclassification Cost
Assumptions
Confusion Model
Best Cut-off Value:
Association Rules
The model achieves better results than a random selection (1.2 X).
The confidence value indicates a 51.24% confidence that a consequent will be found
By: SUMA RAO
SELINA SAPPOR
AIBESSE TESSEMA
Conclusions and Limitations:
Limitations/ Observations:

Dogs may be serviced at other hospitals/ facilities
Data on geographic regions may be too broad

New dogs to facilities are less likely to be compliant with preventative care

Owners with only one dog less compliant with dog's preventative care
Questions:
Full transcript