SNPD 2015

1. Analysis and Classification of Respiratory Health
Risks with Respect to Air Pollution Levels
Ruhul Amin Dicken
North South University, Bangladesh
SNPD 2015
2015/10/26(Mon.)
Chang Wei-Yuan @ MakeLab Lab Meeting
Keywords: data mining; health problem; decision tree;
air pollution; respiratory diseases.

2. Outline
§ Introduction
§ Data Description
§ Methodology
§ Evaluation
§ Conclusion
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3. Introduction
§ Air pollution is the harmful materials to cause
adverse effects on human lives.
– more serious with the development of the growing
cities
§ Bangladesh is facing this problem due to
continuous increase of population.
– this study is focused on a developing country, Dhaka
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4. Goal
§ This paper works on the relationship between
the pollutants and the admittance of patients.
– focused on a case of Dhaka, Bangladesh
– K-means method: clustering different air pollutants in
different seasons
– CART method: to classify the patients according to
different rate of admission
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5. Data Description: Air pollutions
§ Air quality data is collected from Dhaka City
monthly
– by CASE (Clean Air and Sustainable Environment)
– collected: air pollutants and meteorological variables
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6. Data Description: Air pollutions
§ Air quality data is collected from Dhaka City
monthly
– by CASE (Clean Air and Sustainable Environment)
– collected: air pollutants and meteorological variables
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stations time SO2 NO2 … solar rainfall …
string datetime float float … float float …

7. Data Description: Diseases
§ Respiratory diseases data is collected from
NIDCH monthly
– for each diseases
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location time Age group COPD
ILD BroCar
string datetime string integer integer integer
• COPD (chronic obstructive pulmonary disease) 慢性阻塞性肺病
• ILD (interstitial lung disease) 肺病變
• Bronchogenic/Bronchial Carcinoma 支氣管癌

8. Methodology
§ Clustering using k-Means algorithm
§ Classification using the CART analysis
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9. Clustering
§ Air quality data
– using k-means with k=4
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10. Clustering
§ Air quality data
– correlation among the Air data attributes
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11. Clustering
§ Respiratory diseases admissions data
– using k-means with k=3
– High (H), Medium (M), Low (L)
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location time Age group COPD
ILD BroCar
string datetime string integer integer integer
location time Age group COPD
ILD BroCar
string datetime string level level level

12. Classification
§ Using the air pollution data and clustered
medical data acting as class label
– to generate a decision tree which would predict the
level of hospital admissions level
– for each Age groups and different diseases
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stations time SO2 … solar … diseases
string datetime float … float … level
location time Age group COPD
ILD BroCar
string datetime string level level level

13. Classification
§ The decision tree generation process was
conducted on the basis of the three different
criterion metrics
– (i) Information Gain
– (ii) Gini Index
– (iii) Gain Ratio
§ Then the two best trees were selected in our
results.
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14. Evaluation
§ ILD for 24-49 Age group
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15. Evaluation
§ ILD for 50+ Age group
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16. Evaluation
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• In order for any model to be validated as an
applicable model to real world scenarios it must
have an accuracy higher than 50%.

17. Conclusion
§ The COPD and ILD model came as applicable
but the bronchitis carcinoma gave a model
which was not applicable in real life due to low
accuracy.
§ The other factors related to the diagnosis of
disease play more important role and levels of
air pollution alone is not enough to create a
sufficient classification model.
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18. Thanks for listening.
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2015/10/26 (Mon.)
Chang Wei-Yuan @ MakeLab Lab Meeting
v123582@gmail.com