Study and Analysis of Sickle Cell Disease of South Gujarat region with the use of Data Mining Classification Techniques

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1 Study and Analysis of Sickle Cell Disease of South Gujarat region with the use of Data Mining Classification Techniques Ashokkumar Vijaysinh Solanki Research Scholar, JJT University, Rajasthan, India Abstract Data mining applications play major role in the field of healthcare sector. Data mining techniques assist to take high level decision for medical sector and it is necessary because healthcare sector have large volume of information and we have to filter out unnecessary information. This paper is towards the field of Data mining techniques to study and analysis about the sickle cell disease highly affected in tribal zone of South Gujarat. Through classification techniques, we can generate mining model. With the help of producing mining model I have done detail classification of Blood group, Sub caste and Area wise. This research work focused on how prevention steps can be taken in the way to minimize the percentages of disease. Keywords Classification, Data mining, Decision Tree, Sickle Cell 1. INTRODUCTION Data mining applications are widely use in area of healthcare sector. This paper focus about genetic blood disorder disease known as Sickle Cell Disease (SCD). SCD is a hereditary anemia, predominantly seen amongst various tribal populations of India. SCD is found all over the world, particularly amongst people migrated from Malaria. According to the hypothesis, it is a natural mutation in Hemoglobin molecule to protect RBCs from malarial vermin by making them a little rigid, so that malarial vermin cannot enter into RBCs [1]. Sickle Cell gene is mainly present amongst tribal people, who originated from malaria endemic forest areas. SCD occurs due to inherited abnormal hemoglobin (Hb) gene, which produce Hb-S (Hb-Sickle). Due to the presence of Hb-S and because of its abnormality converts RBCs into rigid-brittle half moon and it is known as Sickle[4]. This sickle shaped instead of normal soft round shape, which is the main cause of complication of Sickle Cell disease. There is no cure so far for sickle cell disease and it is serious issue for healthcare area throughout the world. It can be reduce with the help of marriage counseling and prenatal diagnosis. Entire world including India suffering from this disease and in Gujarat state especially South Gujarat area is highly affected with this disease. Table 1 shows Sickle Cell affected state in India. With respect data shows in Table 1; 34% tribal community people of Gujarat state suffering from this disease. Table 1: Shows Sickle Cell affected state in India [1] State Name Gujarat 34 Rajasthan 18 Uttar Pradesh 18 Bihar 31 Assam 2 Orissa 12 Andhra Pradesh 34 Tamilnadu 31 Kerala 23 Karnataka 23 Maharashtra 33 % of Sickle Cell 2. Scope and Purpose of Research Large volume of healthcare data needs to be convert into information and finally it turn as knowledge, which can be useful for cost cutting and more helpful for decision maker. Without Data mining techniques such task is very difficult. Application of data mining, knowledge discovery and database techniques are very beneficial but highly challenging in the field of medical and health care. 688

2 SCD is highly affected in southern region of Gujarat specifically in tribal community people situated near by forest area. Through my research we have collected information from various sources eventually we can aware maximum SCD patient regarding disease and kind of prevention that they have to consider for rest of life. My proposed research model will be helpful to the society, medical sector and Government department for future enhancement and improvement in the current system. Proposed system generate model using decision tree classification algorithm. Using classification it classified blood groups and finally we can categorize disease on the basis of blood group attribute. 3. Data Mining Applications in Healthcare In healthcare sector thousand of records are being captured for healthcare processes in the form of Electronic Records(ER). As a result, data mining has play major role for healthcare world. In my research work I have covered more than 1, 00,000 records, which is related to SCD. Through Data Mining Techniques we can do the following. It helps to improve patient care and ultimately helpful for cost cutting. It perform statistical analysis for healthcare data. We can do quality data assessment using data preprocessing, cleaning, missing value treatment etc. It allows exchanging healthcare related information. Using Classification techniques we can generate decision tree which can be helpful for prediction of disease. My research is related to classification of Sickle Cell Disease on the basis of age and blood group as key attributes. Proposed model generate primary classification using J48 algorithm using WEKA data mining tool. 4. Literature Survey SCD is sensitive and serious medical issue around the world. In India Gujarat state is highly affected area and specifically South Gujarat is suffering from this disease. Gujarat Government looks after SCD control program and they screened more than 18 lakhs populations. They found that more than 11% people suffering from either Sickle Cell Trait or Sickle Cell Disease [3]. Sickle Cell Trait or Disease commonly found in tribal community of South Gujarat. Research article published by Patel Ashwin et. al and they performed HPLC test on more than 3 lakh person from all over Gujarat which included tribal community people and non-tribal community people. They found that more number of peoples of tribal community suffering from SCT or SCD compare to non-tribal community [9]. One research article published by Mihir Prafulbhai Rupani et. al from medical college Surat. They have performed test on school children from Zankhvav village. They found that two sub caste namely Chaudhri and Vasava have more number of students suffering from this disease compare to other sub caste students [10]. Data mining techniques are useful in healthcare sector. Literature review in Data Mining, I found that decision tree algorithm for predictive model for machine learning is suitable for classification technique. They explain about ID3 and C4.5 algorithm for performing classification using decision tree. I found following things in my review of literature. - Data mining techniques are widely used in field of healthcare. Classification techniques are most popular techniques for medical sector. - As we know that SCD is major medical issue in Gujarat sector. Tribal area people suffering from this disease. Gujarat government doing remarkable job in this area but it required improvement in system with the help of Data Mining techniques using agent based mining tools. - For applying data for mining process different mining tools are available in market and I found WEKA, ORANGE and TANAGRA are most powerful mining tools. As above stated problem I understood and believe that there is a need of research in such area using Data mining software and it can be helpful for society and medical sector. 5. Research Methodology Data is important part for any systems. Collection of proper and meaningful data always assist to produce better result. As a part of research methodology I gathered data from different sources. Gujarat government took good initiative to conduct screening programme for this disease. Before applying Data mining algorithm we need to perform data preprocessing task. I have done missing value treatment to remove unnecessary tuples and columns from dataset. In my research Age and Blood Groups are vital attributes and must not have missing or blank value. After data preprocessing I performed Data mining decision tree algorithm using J48 implemented in WEKA mining tool. J48 works on the basis of ID3 decision tree and it produce tree on the basis of defined class label. Classification done through decision tree is depending on Entropy of attributes. Highest Entropy value attribute consider as root node and follow recursive procedure to get leaf node as class label. Table 2 shows sample data of proposed research method. WEKA J48 produce decision tree classification which shows that O+Ve and A+Ve blood groups are more risky and sensitive compare to other blood groups. This proposed model classified data on the base of blood group attribute. Using this model I got primary result that blood group A+Ve and O+Ve are more sensitive. Figure 1 shows primary classification done through J48 classifier. This primary result helps to extends model for multiple attributes such as Sub caste, Blood group, Gender and Area. 689

3 Table 2: Shows Sample Sickle Cell Disease Dataset Age Sub Caste Sex Marital Status Area (Taluka) Blood Group 14 M Koli No Chikhli A+Ve 17 M Koli No Chikhli O+Ve 30 F Dhodiya No Chikhli A+Ve 6 F Dhodiya No Navsari O+Ve 12 F Dhodiya No Navsari A+Ve 32 F Dhodiya Yes Navsari O+Ve 28 F Dhodiya Yes Navsari A+Ve 11 F Gamit No Khergam AB+Ve 22 F Dhodiya Yes Khergam O+Ve 5.1 Pseudo code for extension of classification done through J48 Inputs: Sickle Cell Disease Dataset (SCDD) (Here Caste attributes have different head such as ST, OBC, SC. Subcaste attribute having different head like Dhodiya, Gamit etc and Area having six different head like Navsari, Chikhli, Gandevi, Khergam, Vansda and Jalalpore. Blood group restricted only for O+Ve and A+Ve. Status belongs to Risky or Not risky) Output: Sub caste, Area and Gender wise classification Variable Used in Pseudo Code Gsubcast->Indicates value for Sub caste Gtaluka->Indicates value for Area Gbloodgroup -> Indicates value for blood group Begin Input values for Gsubcast, Gtaluka and Gbloodgroup Repeat For each instances of SCDD [Filter out records as per given inputs] If Gsubcast=Subcaste and Gtaluka=Taluka and Gbloodgroup=Blood group and Status = Risky and Age <=15 Then [Categorized blood group further in sub caste, area wise and Gender wise] If Sex=Male then Compute number of occurrences of Male for given criteria Else Compute number of occurrences of Female for given criteria End if End If End of For End Figure 1: Shows primary classification using J48 I have implemented pseudo code in VB.NET as front end SQL Server as backend. Using primary result done using J48 decision tree classifier, I got detail classification as per given criteria such as Blood Group, Sub caste and Area. Figure 2 shows result of extension of J48. Proposed pseudo code expanded blood group in detail classification on the basis of other dependable attributes. Following pseudo code implementation of extension of primary classification and we get details classification. Figure 2: Shows result of extension of J48 classifier for Dataset 690

4 I examined 847 instances for extension work of J48 and result shows that SCD is highly affected for A+Ve and O+Ve blood group. With respect to figure % person suffering from this disease which is belongs to O+Ve and A+Ve blood group. Region in that way they can take prevention steps which can be helpful for minimizes the disease and it provide better rest of life. Future Scope of the Study: For this research data collected from different sources. Gathered information is not enough and it is in unstructured manner. If information is proper and good enough models can produce better result. This data mining model focused on one attribute such as Age. It can be implementing for other attributes. Limitation: This research works belongs to blood disorder disease, so model could not work for other disease. Figure 3: Shows Blood Group wise classification At the time of data collection I found that there is no background history of persons which includes treatment taken and need for further test. Lack of enough information lead to effect on performance of model. ACKNOWLEDGMENT I wish to acknowledge and thanks very much to my Research guide Dr. Jigar V. Patel, Associate Professor, Kalol Institute of Technology & Research Centre, Kalol for his kind support and encouragement. Figure 4: Shows Caste wise percentages of Disease With respect to above figure 4 it shows information caste wise. I found that sub caste Dhodiya is highly affected from this disease % person belongs to this sub caste suffering from collected samples. 6. CONCLUSION This research paper shows that how Data mining applications can be useful in the field of healthcare. Sickle Cell Disease is genetic blood disorder problem and no cure for this disease. Only prevention steps assist for better life. Paper emphasis on J48 decision tree classification techniques using WEKA implementation. Using J48 classification I got primary model for my study and it concluded that certain blood groups are more prone to disease. Further I extended result of J48 decision tree and I got detail classification and it concluded that certain sub caste have high percentages of disease in specific region of South Gujarat. Proposed model concluded that both healthcare sector and Gujarat government can focus on specific Sub caste and Without support and encouragement of my entire CASPS family it won t be possible. I wish to acknowledge my entire CASPS family which helps me to work hard towards producing this work. REFERENCES [1] Sickle Cell Anemia, Gujarati Version, Hani Printers, Ahemedabad [2] Dr. Carlo kopp, Data Mining and Knowledge Discovery Techniques [3] Sickle Cell Anemia Control Program, Health & Family Welfare Dept, Government of Gujarat [4] Sickle Cell Disease and Sickle Cell Anaemia by Dr. Louise Newson, emis press, pp 1-8 [5] Remco R. Bouckaert, Eibe Frank, Mark Hall, Richard Kirkby, Peter Reutemann, Alex Seewald and David Scuse, WEKA Manual for Version ,2013 [6] Mohd Fauzi bin Othman, Thomas Moh Shan Yau, Comparison of Different Classification Techniques Using WEKA for Breast Cancer, IFMBE Proceedings Vol. 15, pp , 2007, [7] Trilok Chand Sharma and Manoj Jain, WEKA Approach for Comparative Study of Classification Algorithm, International Journal 691

5 of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 4, April 2013, pp , ISSN (Print) : [8] Pooja Sharma and Asst. Prof. Rupali Bhartiya, Implementation of Decision Tree Algorithm to Analysis the Performance, International Journal of Advanced Research in Computer and Communication Engineering Vol. 1, Issue 10, December 2012, ISSN (Print) : , pp [9] Ashwinkumar. U. Mand Dr. Anandakumar. K. R,(2010), Ethical and Legal Issues for Medical Data Mining, ISSN: International Journal of Computer Applications Volume 1 No. 28, pp [10] Mihir Prafulbhai Rupani(2012), Bipin Chandubhai Vasava, Kallol Harichand Mallick, Vaibhav Vasant Gharat and Rajkumar Bansal, Department of Community Medicine, Surat Municipal Institute of Medical Education & Research (SMIMER), Surat.Online Journal of Health and Allied Sciences, Peer Reviewed, Open Access, Free Online Journal, South India : ISSN , Volume 11, Issue 2; Apr-Jun 2012 Author Profile Ashokkumar Vijaysinh Solanki, completed M.C.A from Bhavnagar University, Gujarat. He has 17 years teaching experience. He is working with College of Applied Sciences & Professional Studied, Chikhli, Gujarat since He is member of board of studies for faculty of Computer Science and Information Technology affiliated to Veer Narmad South Gujarat University, Surat. His area of interest is Data Mining, RDBMS and Application Programming. 692