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Analysis and Classification of Respiratory Health
Risks with Respect to Air Pollution Levels
Ruhul Amin Dicken
North South...
Outline
§ Introduction
§ Data Description
§ Methodology
§ Evaluation
§ Conclusion
2
Introduction
§ Air pollution is the harmful materials to cause
adverse effects on human lives.
– more serious with the de...
Goal
§ This paper works on the relationship between
the pollutants and the admittance of patients.
– focused on a case of...
Data Description: Air pollutions
§ Air quality data is collected from Dhaka City
monthly
– by CASE (Clean Air and Sustain...
Data Description: Air pollutions
§ Air quality data is collected from Dhaka City
monthly
– by CASE (Clean Air and Sustain...
Data Description: Diseases
§ Respiratory diseases data is collected from
NIDCH monthly
– for each diseases
7
location tim...
Methodology
§ Clustering using k-Means algorithm
§ Classification using the CART analysis
8
Clustering
§ Air quality data
– using k-means with k=4
9
Clustering
§ Air quality data
– correlation among the Air data attributes
10
Clustering
§ Respiratory diseases admissions data
– using k-means with k=3
– High (H), Medium (M), Low (L)
11
location ti...
Classification
§ Using the air pollution data and clustered
medical data acting as class label
– to generate a decision t...
Classification
§ The decision tree generation process was
conducted on the basis of the three different
criterion metrics...
Evaluation
§ ILD for 24-49 Age group
14
Evaluation
§ ILD for 50+ Age group
15
Evaluation
16
• In order for any model to be validated as an
applicable model to real world scenarios it must
have an accu...
Conclusion
§ The COPD and ILD model came as applicable
but the bronchitis carcinoma gave a model
which was not applicable...
Thanks for listening.
18
2015/10/26 (Mon.)
Chang Wei-Yuan @ MakeLab Lab Meeting
v123582@gmail.com
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Analysis and Classification of Respiratory Health Risks with Respect to Air Pollution Levels

SNPD 2015

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Analysis and Classification of Respiratory Health Risks with Respect to Air Pollution Levels

  1. 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. 2. Outline § Introduction § Data Description § Methodology § Evaluation § Conclusion 2
  3. 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 3
  4. 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 4
  5. 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 5
  6. 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 6 stations time SO2 NO2 … solar rainfall … string datetime float float … float float …
  7. 7. Data Description: Diseases § Respiratory diseases data is collected from NIDCH monthly – for each diseases 7 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. 8. Methodology § Clustering using k-Means algorithm § Classification using the CART analysis 8
  9. 9. Clustering § Air quality data – using k-means with k=4 9
  10. 10. Clustering § Air quality data – correlation among the Air data attributes 10
  11. 11. Clustering § Respiratory diseases admissions data – using k-means with k=3 – High (H), Medium (M), Low (L) 11 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. 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 12 stations time SO2 … solar … diseases string datetime float … float … level location time Age group COPD   ILD BroCar string datetime string level level level
  13. 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. 13
  14. 14. Evaluation § ILD for 24-49 Age group 14
  15. 15. Evaluation § ILD for 50+ Age group 15
  16. 16. Evaluation 16 • 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. 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. 17
  18. 18. Thanks for listening. 18 2015/10/26 (Mon.) Chang Wei-Yuan @ MakeLab Lab Meeting v123582@gmail.com

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SNPD 2015

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