Abstract:

Diabetes is an illness caused because of high glucose level in a human body. Diabetes should not be ignored if it is untreated then Diabetes may cause some major issues in a person like: heart related problems, kidney problem, blood pressure,eye damage and it can also affects other organs of human body.

Diabetes can be controlled if it is predicted earlier. To achieve this goal this project work we will do early prediction of Diabetes in a human body or a patient for a higher accuracy through applying, Various Machine Learning Techniques. Machine learning techniques Provide better result for prediction by constructing models from datasets collected from patients.

In this work we will use Machine Learning Classification and ensemble techniques on a dataset to predict diabetes. Which are K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT),Support Vector Machine (SVM), Gradient Boosting (GB) and Random Forest (RF).

The accuracy is different for every model when compared to other models. The Project work gives the accurate or higher accuracy model shows that the model is capable of predicting diabetes effectively. Our Result shows that Random Forest achieved higher accuracy compared to other machine learning techniques.

Procedure of Proposed Methodology:

Step1: Import required libraries, Import diabetes dataset.
Step2: Pre-process data to remove missing data.
Step3: Perform percentage split of 80% to divide dataset as Training set and 20% to Test set.
Step4: Select the machine learning algorithm i.e. KNearest Neighbor, Support Vector Machine, Decision Tree, Logistic regression, Random Forest and Gradient boosting algorithm.
Step5: Build the classifier model for the mentioned machine learning algorithm based on training set.
Step6: Test the Classifier model for the mentioned machine learning algorithm based on test set.
Step7: Perform Comparison Evaluation of the experimental performance results obtained for each classifier.
Step8: After analyzing based on various measures conclude the best performing algorithm.