Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms

Abstract

Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.

Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models

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Individual prediction of hemispheric similarity of functional connectivity during normal aging

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“…Esses trabalhos com IA se aproximam muito desse cenário do "mundo real", permitindo que um computador se concentre na verdadeira origem da doença de um paciente mesmo quando existem diferentes possibilidades. Isso é possível quando se apresenta um modelo com um amplo diagnóstico diferencial de possíveis doenças (BATTINENI et al, 2021). ambientes especializados e de pesquisa (JAMES et al, 2021).…”