Early Prediction of Diabetes Using Feature Selection and Machine Learning Algorithms

Diabetes has become one of the most common diseases in middle- and low-income countries. Machine learning (ML) and data mining techniques have recently been used to predict diabetes with a high success rate. As a result, medical professionals seek a dependable method for predicting diagnosis. Of course, the feature selection process may be considered a global combinatorial optimization problem in machine learning. The number of features is reduced, irrelevant, noisy, redundant data are removed, and classification accuracy is acceptable. This work uses particle swarm optimization (PSO) to implement feature selection, followed by performance comparison. After that, three medical datasets are used to compare the performance of several machine learning methods. Standard approaches are used to determine the optimum technique for the three datasets. The best results for three datasets are reported for each scheme. The primary goal is to assess the validity of each algorithm's data classification in terms of efficiency and effectiveness in terms of accuracy, sensitivity, and specificity. Decision Tree, Random Forest, and Naïve Bayes deliver the highest accuracy with the lowest mistake rate, according to the findings of the experiments. Machine learning may classify and determine which instances should be sent to medical for further evaluation and treatment with high accuracy. Using such an algorithm on a global scale could help minimize the number of people diagnosed with diabetes.