Artificial intelligence-based approaches for improving the diagnosis, triage, and prioritization of autism spectrum disorder: a systematic review of current trends and open issues

The diagnostic process for Autism Spectrum Disorder (ASD) typically involves time-consuming assessments conducted by specialized physicians. To improve the efficiency of ASD screening, intelligent solutions based on machine learning have been proposed in the literature. However, many existing ML models lack the incorporation of medical tests and demographic features, which could potentially enhance their detection capabilities by considering affected features through traditional feature selection approaches. This study aims to address the aforementioned limitation by utilizing a real dataset containing 45 features and 983 patients. To achieve this goal, a two-phase methodology is employed. The first phase involves data preparation, including handling missing data through model-based imputation, normalizing the dataset using the Min-Max method, and selecting relevant features using traditional feature selection approaches based on affected features. In the second phase, seven ML classification techniques recommended by the literature, including Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, Gradient Boosting (GB), and Neural Network (NN), are utilized to develop ML models. These models are then trained and tested on the prepared dataset to evaluate their performance in detecting ASD. The performance of the ML models is assessed using various metrics, such as Accuracy, Recall, Precision, F1-score, AUC, Train time, and Test time. These metrics provide insights into the models' overall accuracy, sensitivity, specificity, and the trade-off between true positive and false positive rates. The results of the study highlight the effectiveness of utilizing traditional feature selection approaches based on affected features. Specifically, the GB model outperforms the other models with an accuracy of 87%, Recall of 87%, Precision of 86%, F1-score of 86%, AUC of 95%, Train time of 21.890, and Test time of 0.173. Additionally, a benchmarking analysis against five other studies reveals that the proposed methodology achieves a perfect score across three key areas. By considering affected features through traditional feature selection approaches, the developed ML models demonstrate improved performance and have the potential to enhance ASD screening and diagnosis processes.

Section: Introductionmentioning

confidence: 99%

Unlocking the Potential of Autism Detection: Integrating Traditional Feature Selection and Machine Learning Techniques

Hazim Hammed,

Albahri

2023

“…Nearly 2.3 million women were diagnosed with breast cancer in 2020 alone, resulting in 685,000 deaths [1] [2]. One of the main factors for successful and effective treatment and bettering patient outcomes is early detection and accurate diagnosis [3]. Breast cancer detection and grading rely heavily on histopathology images acquired via medical imaging techniques.…”

Section: Introductionmentioning

confidence: 99%

Automated Grading System for Breast Cancer Histopathological Images Using Histogram of Oriented Gradients (HOG) Algorithm

Saher,

Alsaedi,

Al Ibraheemi

2023

Breast cancer is the most common type of cancer in the world, affecting both men and women. In 2023, the American Cancer Society's reported that there will be approximately 297,800 new cases of invasive breast cancer in women and 2,850 in men, along with 55,750 cases of ductal carcinoma in situ (DCIS) in women. Further, an estimated 43,750 deaths are expected from breast cancer, of which approximately 43,180 are among women and 570 are among men. In this paper, we propose an automated grading system for breast cancer based on tumor's histopathological images using a combination of the Histogram of Oriented Gradients (HOG) for feature extraction and machine learning algorithms. The proposed system has four main phases: image preprocessing and segmentation, feature extraction, classification, and integration with a website. Grayscale conversion, enhancement, noise and artifact removal methods are used during the image preprocessing stage. Then the image is segment during the segmentation phase to extract regions of interest. And then, features are extracted from the obtained region of interest using the Histogram of Oriented Gradients (HOG) algorithm. The next, the images are classified into three distinct breast cancer grades based on the extracted features using machine learning algorithms. Moreover, the effectiveness of the proposed system was evaluated and reported using vary evaluation methods and the results showed a remarkable accuracy of up to 97% by the SVM classifier. Finally, the machine learning model is integrated into a website to improve the detection and diagnosis of breast cancer disease and facilitate the access and use of patient data. This will make the work easier for physicians to enhance breast cancer detection and treatment

“…Autism Spectrum Disorder (ASD) is a complex neuropsychiatric disorder that is influenced by genetic factors. The World Health Organization (WHO) estimates that there are 67 million individuals worldwide affected by ASD [1] [2]. Children with autism exhibit various symptoms, including repetitive behaviors, abnormalities in facial and vocal expressions, delayed speech learning, and difficulties in communication [3].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Decision-Making Framework for Sensitively Prioritizing Autism Patients with Moderate Emergency Level

Talib

Albahri

Edoh³

2023

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that requires careful assessment and management. The prioritization of ASD patients involves navigating through complexities such as conflicts, trade-offs, and the importance of different criteria. Therefore, this study focuses on prioritizing patients with ASD in the healthcare setting through an evaluation and benchmarking framework. The aim of this study is to develop a framework that utilizes Multi-Criteria Decision Making (MCDM) methods to assist healthcare professionals in prioritizing ASD patients, particularly those with moderate injury levels. The methodology of the framework outlines several phases, including dataset identification, development of a decision matrix, weighting of 19 ASD criteria using the FWZIC method, ranking 432 patients using the VIKOR method, and evaluating the proposed framework using four sensitivity analysis scenarios. Among the 19 ASD criteria, the criterion 'verbal communication' obtained the highest weight. Additionally, criteria such as 'laughing for no reason', 'nodding', 'patient movement at home', and 'pointing with the index finger' obtained similar higher weights, indicating their potential impact on ASD patients. The experimental results highlight the significance of adjusting ASD weights in influencing the final rankings obtained through the VIKOR method. This emphasizes the need for careful consideration when assigning weights to the 19 ASD criteria to ensure accurate prioritization. Moreover, the framework provides valuable insights into improving the care and support provided to individuals with autism in Iraq. The findings contribute to the existing body of knowledge in the field of autism care prioritization and pave the way for future research and interventions aimed at enhancing the quality of care for individuals with autism in Iraq.