Comparative Analysis of Pure and Hybrid Machine Learning Algorithms for Risk Prediction of Diabetes Mellitus

Author Name(s): Deeksha Kaul, 2 Harika Raju, 3 B.K. Tripathy
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Diabetes, a chronic disease, occurs due to abnormal levels of glucose and insulin in our bodies. The major factors leading to the disease are lifestyle factors such as diet, insufficient physical activity, increased stress levels and obesity. One of the major issue with diabetes is the mildness of its symptoms which delays its diagnosis until the disease has significantly progressed leading to other medical complications like blindness and nerve and kidney damage. This paper compares the efficiency of various machine learning algorithms for predicting the onset of diabetes. The performance analysis is demonstrated on Support Vector Machines (SVM), Deep Neural Networks and Hybrid Deep Learning. The study shows that the classification accuracy is significantly higher for hybrid deep neural networks indicating its better performance. The results also justify that hybrid deep learning has better processing capability and produces accurate results, thus helping in the prediction of many diseases.


Deep Neural Networks, Support Vector Machine, Hybrid Deep Learning, Diabetes mellitus.


Deep learning is an unsupervised learning algorithm which mimics the human brain. The deep network has the capacity to process large datasets, implement complex functions as well as it can work with unlabeled data with small human inputs (training set) and generates the convoluted representation of unprocessed data. For enhanced performance, it has been suggested to combine supervised learning algorithm (SVM) with the deep neural networks. The unsupervised nature combined with complex learning algorithms ensures fast processing and accurate predictions for multi-dimensional data.


It can be concluded from the study that hybrid deep learning provides the most satisfactory results for prediction of diabetes. Least error rate and highest area under the ROC curve, accuracy and precision values provide evidence of better performance as compared to pure SVM and pure Deep Learning models. But even with extensive training, there is a chance of misdiagnosis as the model is not 100% accurate. For future work, the performance of the decision support system can be optimized by training with more extensive data. Newer data cleansing techniques like using Regression Substitution for handling missing values can be used for better accuracy. Furthermore, the current data only focuses on the female population and we intend to explore the trends in the male population of the society as well.

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