Adhd prediction using machine learning algorithms

Abstract

This study analyzes the use of machine learning algorithms to predict Attention Deficit Hyperactivity Disorder (ADHD) based on a number of feature sets. A dataset from online containing demographic information, personality characteristics, and medical indicators was used. Among the classifiers used, the logistic regression model achieved the highest accuracy of 98.17%. Data preprocessing consisted of cleaning, transforming, and encoding features, followed by feature selection and exploratory data analysis. The logistic regression model, trained on the preprocessed dataset, outperformed other classifiers including random forest, voting, gradient boosting, Gaussian Naive Bayes, and decision tree models, with an accuracy of 98.17%. The model showed high precision, recall, and F1-score, indicating that it was effective in distinguishing between people with and without ADHD. The dataset's attributes were important in identifying key predictors of ADHD, providing helpful information for clinical decision-making. Ethical concerns about data privacy and algorithmic bias were addressed all through the study to ensure responsible implementation. Overall, this study shows the potential of machine learning to improve ADHD diagnosis and shows the importance of comprehensive data collection and rigorous model evaluation in healthcare applications.