A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

Aim:

          To apply machine learning techniques result in improving the accuracy in the prediction of Autism Spectrum Disorder

Abstract:

          This research presents a comprehensive machine learning framework designed for the early-stage detection of Autism Spectrum Disorders (ASD). ASD diagnosis is challenging due to its heterogeneous nature and the need for timely intervention. Leveraging the power of machine learning, our framework integrates label encoding, Recursive Feature Elimination (RFE), and Logistic Regression (LR) algorithms to enhance the accuracy and efficiency of ASD detection.

Existing System: 

                 This research aims to create an effective prediction model using different types of ML methods to detect autism in people of different ages. First of all, the datasets are collected, and then the preprocessing is accomplished via the missing values imputation, feature encoding, and oversampling. The Mean Value Imputation (MVI) method is used to impute the missing values of the dataset. Then, the categorical feature values are converted to their equivalent numerical values using the One Hot Encoding (OHE) technique. shows that all four datasets used in this work have an imbalanced class distribution problem. As such, a Random Over Sampler strategy is used to alleviate this issue. After completing the initial preprocessing, the datasets’ feature values are scaled using four different FS techniques i.e., QT, PT, Normalizer, and MAS (see their detailed operations in The feature-scaled datasets are then classified using eight different ML classification techniques i.e., AB, RF, DT, KNN, GNB, LR, SVM, and LDA.

 Proposed System:

                 This research aims to create an effective prediction model using different types of ML methods to detect autism in people of different ages. First of all, the datasets are collected, and then the preprocessing is accomplished the missing values imputation, Label encoding, and oversampling and Create an instance of RFE with the classifier and the desired number of features to select Logistic Regression classification of modeling, performance evaluation, and the results with improved accuracy.