1. The web application utilizes machine learning algorithms to efficiently detect Autism Spectrum Disorder (ASD) in individuals of all age groups, offering a user-friendly and accessible platform for early screening.

2. By integrating multiple sources of data, including behavioral observations, medical records, and genetic data, the application enables comprehensive analysis of ASD traits, leading to more accurate detection and personalized intervention strategies.

3. The hybrid model, incorporating diverse ML algorithms such as Linear Discriminant Analysis (LDA), Naive Bayes (NB), and Support Vector Machine (SVM), among others, maximizes accuracy and efficiency in predicting autism traits, providing healthcare professionals, educators, and researchers with reliable results for informed decision-making and intervention planning.

Description:Title:

Detection of Autism Spectrum Disorder using Machine Learning

Field of the Invention

[0001] The present invention is related to the computer science and machine learning field.

Background

[0002] ASD is a complex neurodevelopmental disorder characterized by difficulties in communication, behavior, and social interaction. Diagnosing ASD can be challenging due to its diverse symptoms and variability in presentation across individuals. Early detection is crucial for intervention and support but can be hindered by the lack of standardized screening tools and the variability in symptoms.
[0003] There has been an observed increase in the prevalence of ASD cases globally. This rise may be due to increased awareness, improved diagnostic methods, and changes in environmental factors. The growing number of ASD cases underscores the importance of developing efficient and accessible screening methods.
[0004] Machine learning algorithms have demonstrated significant potential in healthcare applications, including disease diagnosis and prediction. ML techniques can analyze large datasets, identify patterns, and make predictions, making them well-suited for aiding in ASD diagnosis.
[0005] The motivation for this innovation stems from the need to address the challenges associated with ASD diagnosis, particularly the need for early detection and intervention. By leveraging ML techniques and creating a user-friendly web application, the goal is to provide healthcare professionals, educators, and parents with a tool that can facilitate rapid and accurate ASD screening across all age groups.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0007] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0008] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0009] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.
[0010] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

Objects of the Invention

[0011] Develop a user-friendly and accessible web application leveraging machine learning algorithms for ASD detection in individuals.Integrate diverse data sources such as behavioral observations, medical records, and genetic data within the web application to enable thorough and precise analysis of ASD.
[0012]. Employ feature selection techniques to pinpoint the most pertinent features crucial for ASD detection, while implementing a variety of ML models to optimize accuracy and efficiency in the detection process.Enable healthcare professionals, educators, and researchers to seamlessly upload data and obtain timely, accurate results, empowering them to devise early intervention strategies effectively.

Drawings

Figure 1

Brief Description of the Drawing

[0013] The figure 1 represents working model in the present invention with its prototype.

Detailed Description:

[0014] In figure 1, showing the input parameter; which is to be processed by the system 100.
[0015] Prepare data for machine learning models by cleansing and refining raw data. This crucial step ensures the data is suitable for analysis and model development.
[0016] Utilize real-time data collected by Faiza Nabi and additional data from Kaggle to enrich the dataset.

[0017] Employ various ML algorithms, including Linear Discriminant Analysis (LDA), Naive Bayes (NB), Classification and Regression Trees (CART), K-Nearest Neighbors (KNN), and Logistic Regression (LR), to predict autism traits. Incorporate Support Vector Machine (SVM) for enhanced classification accuracy.
[0018] Synthesize collected data to eliminate irrelevant features and enhance the quality of the dataset.
[0019] Perform predictive modeling to predict class labels based on input data. Train the classifier using a designated training dataset, refine parameters using a validation set, and evaluate classifier performance using an independent test dataset.
[0020] Use a validation set to fine-tune model parameters and optimize model performance before final evaluation.
[0021] Assess the performance of the trained classifier on unseen test data to gauge the effectiveness and generalizability of the model in predicting autism traits accurately.
[0022] In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K- Nearest Neighbours, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering
– Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
[0023] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[0024] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-
exclusive manner, indicating that the referenced elements, components, or
steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
, Claims:We Claim:

1. The web application utilizes machine learning algorithms to efficiently detect Autism Spectrum Disorder (ASD) in individuals of all age groups, offering a user-friendly and accessible platform for early screening.
2. By integrating multiple sources of data, including behavioral observations, medical records, and genetic data, the application enables comprehensive analysis of ASD traits, leading to more accurate detection and personalized intervention strategies.
3. The hybrid model, incorporating diverse ML algorithms such as Linear Discriminant Analysis (LDA), Naive Bayes (NB), and Support Vector Machine (SVM), among others, maximizes accuracy and efficiency in predicting autism traits, providing healthcare professionals, educators, and researchers with reliable results for informed decision-making and intervention planning.