Statistical Analysis and Multimodal Classification on Noisy Eye Tracker and Application Log Data of Children with Autism and ADHD

Öztürk, Mahiye Uluyağmur; Arman, Ayşe Rodopman; Bulut, Gresa Çarkaxhiu; Fındık, Onur Tuğçe Poyraz; Yilmaz, Sultan; Genç, Herdem Aslan; Yazgan, M. Yanki; Teker, Umut; Çataltepe, Zehra

doi:10.31209/2018.100000058

Cited by 5 publications

(1 citation statement)

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“…The system is mainly composed of four modules, which are data preprocessing module, data integration module [17,18], correlation algorithm implementation module, and external query module.…”

Section: Intelligent Medical Diagnosis Methodsmentioning

confidence: 99%

The Role of Medical Big Data and Artificial Intelligence in the Diagnosis of Knee Osteoarthritis with Cardiovascular and Cerebrovascular Diseases

Huan

Pan

Zhou

2022

Wireless Communications and Mobile Computing

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In this Internet era, people use more and more network data, and hospital diagnosis is even more important. The improvement of medical standards has brought about the rapid growth of medical data. How to analyze related symptoms from a large amount of data performing an analysis and accurately diagnosing its symptoms has become one of the difficult problems. This article is aimed at studying the role of medical huge amount of data and artificial intelligence in the diagnosis of knee osteoarthritis combined with cardiovascular and cerebrovascular diseases. To this end, this article proposes to improve data mining methods and proposes a comprehensive analysis of patient data in the field of artificial intelligence for intelligent diagnosis and treatment so that doctors can make accurate judgments about the symptoms of patients and designed related experiments to explore its specific effects. The experimental results in this article show that the improved data mining speed has increased by 17%, the data integrity has increased by 31%, and the proportion of valid data has also increased by 23%, which is very effective for clinical diagnosis.

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Section: Intelligent Medical Diagnosis Methodsmentioning

confidence: 99%