Diabetes prediction using enhanced SVM and deep neural network learning techniques: An algorithmic approach for early screening of diabetes

Abstract

Diabetes, caused by the rise in level of glucose in the blood, has many devices to identify it from blood samples. Diabetes, when unnoticed, may bring many serious diseases like heart attack and kidney disease. In this way, there is a requirement for solid research and learning model enhancement in the field of gestational diabetes identification and analysis. SVM is one of the powerful classification models in machine learning, and similarly, deep neural networks are powerful under deep learning models. In this work, the authors applied enhanced support vector machine and deep learning model deep neural network for diabetes prediction and screening. The proposed method uses a deep neural network obtaining its input from the output of enhanced support vector machine, thus having a combined efficacy. The dataset considered includes 768 patients’ data with eight major features and a target column with result “Positive” or “Negative.” Experiment is done with Python, and the outcome of the demonstration shows that the deep learning model gives more efficiency for diabetes prediction.