Prediction of gestational diabetes diagnosis using SVM and J48 classifier model

  • Authors

    • S Saradha
    • P Sujatha
    2018-04-20
    https://doi.org/10.14419/ijet.v7i2.21.12395
  • GDM, risk factors, classification, SVM, J48, hybrid model.

  • Knowledge Discovery in Databases (KDD) process is also known as data mining. It is a most powerful tool for medical diagnosis. Due to hormonal changes, diabetes may  occur during pregnancy is referred as Gestational diabetes mellitus (GDM). Pregnant Women with GDM are at highest risk of future diabetes, especially type-2 diabetes. This paper focuses on designing an automated system for diagnosing gestational diabetes using hybrid classifiers as well as predicting the highest risk factors of getting Type 2 diabetes after delivery. One of the common   predictive data mining tasks is classification. It classifies the data and builds a model based on the test data values and attributes to produce the new classified data. For detecting GDM and also its risk factors, two classifier models namely modified SVM and modified J48 classifier models are proposed. The data set were collected from various hospitals and clinical labs and preprocessed with discretize filter using weka tool. Missing values are replaced by the suitable values. The final preprocessed data applied in the proposed classifier Model.  The output of the proposed model is compared with all the other existing methodologies. Since the proposed model modified J48 classifier model produces more accuracy and low error rate against other existing classifier models.

     

  • References

    1. [1] Takashi S, “Management of Gestational Diabetes Mellitusâ€, Journal of the Japan Medical Association, Vol.139, No.10, (2011), pp. 2089 -2094.

      [2] Feizi-Derakhshi MR & Zafarani E, “Review and comparison between clustering algorithms with duplicate entities detection purposeâ€, International Journal of Computer Science & Emerging Technologies, Vol.3, No.3, (2012).

      [3] Karthikeyani V, “Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Predictionâ€, International Journal of Computer Applications, Vol.60, No.12, (2012).

      [4] Hasdemir PS, Terzi H & Koyuncu FM, “Recent advances in the diagnosis and management of gestational diabetesâ€, Turkish journal of obstetrics and gynecology, Vol.11, No.3, (2014), pp.181-185.

      [5] Lingaraj H, Devadass R, Gopi V & Palanisamy K, “Prediction of Diabetes Mellitus using Data Mining Techniques: A Reviewâ€, Journal of Bioinformatics & Cheminformatics, Vol.1, No.1, (2015), pp.1-3.

      [6] Iyer A, Jeyalatha S & Sumbaly R, “Diagnosis of diabetes using classification mining techniquesâ€, International Journal of data mining & knowledge management process, Vol.5, No.1, (2015).

      [7] Habibi S, Ahmadi M & Alizadeh S, “Type 2 diabetes mellitus screening and risk factors using decision tree: results of data miningâ€, Global journal of health science, Vol.7, No.5, (2015), pp.304-310.

      [8] Nagarajan S & Chandrasekaran RM, “Design and implementation of expert clinical system for diagnosing diabetes using data mining techniquesâ€, Indian Journal of Science and Technology,Vol.8, No.8, (2015), pp. 771-776.

      [9] Kalaiselvi C & Nasira GM, “Prediction of Heart diseases and cancer in diabetic patients using data mining techniquesâ€, Indian Journal of Science and Technology, Vol.8, No.14, (2015).

      [10] Saradha S & Sujatha P, “Analysis and significance study of clustering techniquesâ€, IJETER, Vol.4, No.9, (2016), pp.31-33.

      [11] Durairaj M & Kalaiselvi G, “Prediction of diabetes using back propagation algorithmâ€, International Journal of Emerging Technology and Innovative Engineering, Vol.1, No.8, (2015).

      [12] Deepika N & Poonkuzhali S, “Design of hybrid classifier for prediction of diabetes through feature relevance analysisâ€, IJISET, Vol.2, No.10, (2015).

      [13] Amin MN & Habib MA, “Comparison of different classification techniques using WEKA for hematological dataâ€, American Journal of Engineering Research, Vol.4, No.3, (2015), pp.55-61.

      [14] Saradha S & Sujatha P, “A performance evaluation of classification algorithms for diagnosing gestational diabetesâ€, JARDCS, (2017), pp.268 -274.

      [15] Bortolon LNM, Triz LDPL, de Souza Faustino B, de Sá LBC, Rocha DRTW & Arbex AK, “Gestational diabetes mellitus: new diagnostic criteriaâ€, Open Journal of Endocrine and Metabolic Diseases, Vol.6, No.01, (2016), pp.13-19.

      [16] Sujatha P & Saradha S, “A study of data mining concepts and techniquesâ€, IJAER, Vol.9, No.27, (2014), pp.9648 -9651.

      [17] Marlina L, “Data mining classification comparison(Naive bayes and C4.5 Algorithms)â€, International Journal of Engineering Trends and Technology, Vol.38, No.7, (2017).

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  • How to Cite

    Saradha, S., & Sujatha, P. (2018). Prediction of gestational diabetes diagnosis using SVM and J48 classifier model. International Journal of Engineering & Technology, 7(2.21), 323-326. https://doi.org/10.14419/ijet.v7i2.21.12395