A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning

Shinde, Anita Vikram; Patil, Dipti D.

doi:10.1016/j.health.2023.100211

Cited by 12 publications

(3 citation statements)

References 30 publications

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“…In [15], a recommender system for the automation of clinical practice guidelines was proposed. Other studies in health care focused on recommender systems for the treatment of diseases, prediction of diseases and management of patients' diets [16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Social Filtering Model for Event Recommendation Services

Oyemade,

Aworonye

2024

AJAI

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In the contemporary time, technology has made the determination and discovery of human preferences, priorities and personal inclinations possible through the use of recommender systems. Activities of users on the internet can be monitored, extracted, stored, analyzed and used by the recommender systems for suggesting future events to users on the web. This paper aims at developing and analyzing a model for event services recommendation for visitors to events. Event seekers, organizers and event service providers get notified, plan and book for upcoming events from their comfort zones without hassles of gallivanting nooks and crannies to enquire about prospective events. There is not any compelling need to interface with under-enthusiasts and intermediaries in the course of organizing, visiting and providing services for an event. However, it is obvious that massive amount of available information on the web exhibit frustrating attributes, hence it is increasingly a difficult task for users to find the content of interest; in other words, a huge chunk of information undiscovered on the network is left behind as “dark information”. In context, event service recommendation uses deep learning social filtering base techniques which adopt similarity computation measures with a bias for Pearson correlation coefficient, cosine similarity, and Euclidean similarity to recommend related and most relevant events/services to the targeted online audience. In this paper, the aim is to develop a deep learning model which integrates social filtering technique for enhancing the quality of event recommendation for users. A model based on the deep learning algorithm of multilayered perceptron and Neural Collaborative Filtering is proposed for event recommender services. The results from various simulations using meetup website dataset shows that the proposed model performs better than other techniques. The results yield 70% accuracy, 66% precision and 98% recall.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Social Filtering Model for Event Recommendation Services

Oyemade,

Aworonye

2024

AJAI

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“…The need for efficient and effective medical diagnostic systems in the context of ASD detection and treatment is paramount [13]. Healthcare professionals often spend considerable time documenting and processing extensive remarks related to patient behavioral assessments.…”

Section: Related Workmentioning

confidence: 99%

Unleashing the Potential of Artificial Bee Colony Optimized RNN-Bi-LSTM for Autism Spectrum Disorder Diagnosis

Jugunta,

El-Ebiary,

Saravanan

et al. 2023

IJACSA

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The diagnosis of Autism Spectrum Disorder (ASD) is a crucial, drawn-out, and sometimes subjective procedure that calls for a high level of knowledge. Automation of this diagnostic procedure appears to be possible because to recent developments in machine learning techniques. This paper presents a unique method for improving the performance of a Recurrent Neural Network with a Bidirectional Long Short-Term Memory (RNN-BiLSTM) model for ASD diagnosis by utilizing the power of Artificial Bee Colony (ABC) optimization. Because Python software is used to carry out the implementation, accessibility and adaptability in clinical contexts are guaranteed. The suggested approach is thoroughly contrasted with current techniques, such as ABC optimization for feature extraction, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models, and Transfer Learning, in order to highlight its effectiveness. The outcomes demonstrate the superiority of the RNN-BiLSTM over other methods, with much greater accuracy and precision. Combining RNN-BiLSTM with ABC optimization demonstrates not just cutting-edge accuracy but also excellent interpretability. By using this sophisticated model's capabilities, an outstanding diagnosis accuracy of 99.12% is attained, which is 2.77% higher than previous approaches. The model helps physicians comprehend the diagnosis process by highlighting important characteristics and trends that influence its conclusion. Additionally, it lessens the subjectivity and unpredictability involved in human diagnosis, which may result in quicker and more accurate diagnoses of ASD. The research emphasizes how well the Artificial Bee Colony optimized RNN-BiLSTM model diagnoses autism spectrum disorder. By integrating AI-driven diagnostic tools into clinical practice, this research improves early diagnosis and intervention for ASD.

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“…A centralized framework for autism disorder detection (3) proposed and gives 89.23% of accuracy with the Random Forest classification model. A multiclassifier recommended system (4) proposed in the decision making process of autism detection. This system yields maximum accuracy in the Decision Tree and Random Forest model.…”

Section: Introductionmentioning

confidence: 99%

Enco – Standardization Data Pre-Processing Technique in Autism Spectrum Disorder Detection

Kavitha,

Kasthuri

2023

IJST

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Background/Objectives: The goal of this study was to create an Enco-Standardization technique that would produce accurate data and improve the diagnosis of Autism Spectrum Disorder (ASD).This method uses mean values to replace missing values in a dataset and improves them by combining label encoding and conventional scaling techniques. Methods: The ASD dataset, which has 704 instances and 21 attributes, is used in this study. Training and testing are divided by the dataset (80%-20%). As an imputation strategy in this dataset, missing values are located and replaced with the mean value. Attributes are encoded using the Enco-Standardization methodology using a label encoding technique that changes non-numeric variables into numeric ones. After that, the data were scaled into a machine-readable format to standardise it. Different machine learning classifier models are compared to the hybrid strategy of encoding and scaling techniques. Based on the accuracy found using machine learning classifier models, the dataset acquired using the Enco-Standardization technique is assessed. Findings: The dataset needs to be accurate and relevant in order to increase accuracy and decrease computing time. The findings of the Enco-Standardization methodology showed a good pre-processing method with accuracy values of 98% for Naive Bayes (NB), 71% for K Nearest Neighbour (KNN), 74% for Support Vector Machine (SVM), 97% for Linear Regression (LR), 100% for Decision Tree (DT), and 100% for Random Forest (RF). The deletion of missing values improves performance in KNN (94%), SVM (95.9%), LR, DT, and RF (100%) but decreases the number of instances in the dataset, rendering the model ineffective. Novelty: The data in a dataset are transformed and encoded using the proposed Enco-Standardization pre-processing technique, which increases the precision of the data analysis process in ASD prediction. Data discrepancies are avoided by using this eco-standardization technique.

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A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning

Cited by 12 publications

References 30 publications

Deep Learning Social Filtering Model for Event Recommendation Services

Deep Learning Social Filtering Model for Event Recommendation Services

Unleashing the Potential of Artificial Bee Colony Optimized RNN-Bi-LSTM for Autism Spectrum Disorder Diagnosis

Enco – Standardization Data Pre-Processing Technique in Autism Spectrum Disorder Detection

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