EARLY PREDICTION OF TYPE2 DIABETES USING SUPERVISED MACHINE LEARNING ALGORITHM

Diabetes is a common chronic health condition with significant implications on a global scale.Early and accurate prediction of the health condition of the patient suffering from diabetes is extremely beneficial in enhancing the health conditions of the patient.Blood glucose levels are significant factors in the determination of the presence of the health condition known as diabetes, which is a type of metabolic disorder.Machine learning algorithm performance is significant in enhancing the diagnosis and prognosis of various health conditions.The classification algorithms, namely Random Forest (RF), Decision Tree (DT), Naïve Bayes (NB), and Logistic Regression (LR), were used for the experiment.The performance measures like area under the curve, F1 score, recall, accuracy, and precision are used for testing the performance of the algorithm.The results of this investigation showed that RF performs better than the other models, DT, NB, and LR, in terms of accuracy (77.49%), precision (68.97%), and sensitivity (59.46%) and AUC-ROC of 83.8 Diabetes is a global problem that impacts individuals everywhere.Predictive models based on machine learning can help detect diabetes earlier before it becomes a serious condition.Early Prediction of Type2 Diabetes Using Supervised Machine Learning Algorithm https://iaeme.com