PREDICTION OF DIABETES EMPOWERED WITH FUSED MACHINE LEARNING

Abstract : In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection.This article presents a model using a fused machine learning approach for diabetes prediction
 EXISTING SYSTEM :
 ? The existing fuzzy decision system has achieved the accuracy of 94.87, which is higher than the other existing systems ? Through this diagnosis model, we can save several lives. ? Moreover, the death ratio of diabetes can be controlled if the disease is diagnosed and preventative measures are taken in early-stage.
 DISADVANTAGE :
 ? These algorithms are selected in this research after some initial experiments where we have found these techniques more effective for this problem. ? We used various accuracy measures, including: accuracy, specificity, sensitivity, precision, and F1 score in the Performance Evaluation stage. ? If the proposed model does not meet the learning requirements, it will be retrained
 PROPOSED SYSTEM :
 ? The proposed fused ML model has a prediction accuracy of 94.87, which is higher than the previously published methods. ? The dataset used in the proposed system comes from the (University of California Irvine) UCI Machine Learning repository compiled by the hospital of Sylhet, Bangladesh. ? proposed a model that uses four ML algorithms – Support Vector Machine (SVM), C4.5 Decision Tree, K-Nearest Neighbor (KNN), and Naive Bayes to predict diabetes
 ADVANTAGE :
 ? The dataset used in this research is divided into training data and testing data with a ratio of 70:30 respectively. ? The dataset used in the comes from the (University of California Irvine) UCI Machine Learning repository compiled by the hospital of Sylhet, Bangladesh.
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