• Logo
  • HamaraJournals

Diabetes Diagnosis Using Machine Learning

Boshra Farajollahi, Maysam Mehmannavaz, Hafez Mehrjoo, Fateme Moghbeli, Mohammad Javad Sayadi



Introduction: Diabetes is a disease associated with high levels of glucose in the blood. Diabetes make many kinds of complications, which also leads to a high rate of repeated admission of patients with diabetes. The aim of this study is to diagnose Diabetes with machine learning techniques.

Material and Methods: The datasets of the article contain several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age. The main objective of the machine learning models is to classify of the diabetes disease.

Results: six classifiers have been also adapted and compared their performance based on accuracy, F1-score, recall, precision and AUC. And Finally, Adaboost has the most accuracy 83%.

Conclusion: In this paper a performance comparison of different classifier models for classifying diagnosis is done. The models considered for comparison are logistic regression, Decision Tree, support vector machine (SVM), xgboost, Random forest and ada boost. Finally, in the comparison flow, Adaboost, Logistic Regression, SVM and Random Forest, usually has had a high amount; and their amounts has little differences normally. 


Warke M, Kumar V, Tarale S, Galgat P, Chaudhari D. Diabetes diagnosis using machine learning algorithms. International Research Journal of Engineering and Technology. 2019; 6(3): 1470-6.

Kavakiotisab I, Tsave O, Salifoglou A, Maglaveras N, Vlahavasa I, Chouvarda I. Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal. 2017; 15: 104- 16.

Benbelkacem S, Atmani B. Random forests for diabetes diagnosis. International Conference on Computer and Information Sciences. IEEE; 2019.

Sun YL, Zhang DL. Machine learning techniques for screening and diagnosis of diabetes: A survey. Tehnički Vjesnik. 2019; 26(3): 872-80.

Maniruzzaman M, Rahman MJ, Ahammed B, Abedin MM. Classification and prediction of diabetes disease using machine learning paradigm. Health Inf Sci Syst. 2020; 8(1): 7. PMID: 31949894 DOI: 10.1007/s13755-019-0095-z

Pujianto U, Setiawan AL, Rosyid HA, Salah AMM. Comparison of naïve Bayes algorithm and decision tree C4. 5 for hospital readmission diabetes patients using hba1c measurement. Knowledge Engineering and Data Science. 2019; 2(2): 58-71.

Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, et al. Feature selection: A data perspective. ACM Computing Surveys. 2017; 50(6): 1-45.

Jia M, Tian F. Readmission prediction of diabetic based on convolutional neural networks. International Conference on Computer and Communications. IEEE; 2019.

Moshtaghi Yazdani N, Yazdani Seqeloo A. Diabetes diagnosis via XCS classifier system. Iran Med Inform. 2014; 3(1): 1-8.

DOI: http://dx.doi.org/10.30699/fhi.v10i1.267


  • There are currently no refbacks.