Diabetes Prediction with Machine Learning with Python

S. Rakesh  Kumar; Kruthi. G; V. Supraja

doi:10.32628/CSEIT2390651

Authors

Dr. S. Rakesh Kumar Assistant Professor, Department of Computer Science Engineering, GITAM University, Karnataka, India Author
Kruthi. G Department of Computer Science Engineering, GITAM University, Karnataka, India Author
V. Supraja Author

DOI:

https://doi.org/10.32628/CSEIT2390651

Keywords:

Logistic Regression, SVM, ANN, ML Techniques

Abstract

This article introduces an innovative approach leveraging a combination of machine learning techniques to enhance early diabetes detection, a crucial step given the disease's global impact. With the prevalence of sugar and fats in contemporary diets contributing to an increased diabetes risk, early identification through symptom recognition is key. The proposed method integrates Using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms, patient data is analyzed to classify diabetes diagnoses as either affirmative or negative. The study involves the utilization of a dataset that has been divided into 70% for training data and 30% for testing data. The outputs from the SVM and ANN models serve as inputs for a fuzzy logic system, which then makes the final diagnosis determination. This hybrid model is stored on a cloud platform for accessibility and uses real-time patient data for predictions. The combined machine learning model demonstrates superior accuracy in predicting diabetes compared to existing methods.

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References

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