Medicine and Health Sciences Commons^™

Applying Artificial Neural-Network Model To Predict Psychiatric Symptoms, Elaheh Allahyari, Narges Roustaei

BioMedicine

Introduction: Mental disorders result in mental disabilities and discomfort in the affected person as they affect both thinking and behavior. Therefore, more vulnerable people must first be identified to improve the psychological level of society.

Aim: This study aims to determine the importance of gender, employment, education, place of residence, and age in predicting mental disorders using artificial neural network modeling.

Methods: The pattern held between variables in this study will be identified using multilayer feed-forward back-propagation neural networks with five inputs and 10 outputs. To determine the neural network with the least sum of square errors, we evaluated the …

Predicting Mental Health Of Prisoners By Artificial Neural Network, Elahe Allahyari, Mozhgan Moshtagh

BioMedicine

Introduction: Maintaining and improving prisoners’ health and their rehabilitation can be effective steps towards eliminating health inequalities and approaching the UN’s Sustainable Development Goals. Accordingly, identifying protective factors and health barriers of this vulnerable group and changing the prison into an environment that can deliver health interventions tailored to the needs of inmates can provide the basis for attaining justice in health.

Purpose: This study builds on an artificial neural network model to determine the effect of demographic, psychological, criminological, and physical activity factors on prisoners’ general health.

Methods: The study detected the patterns between variables using a neural network …

A Review On Recent Advancements In Diagnosis And Classification Of Cancers Using Artificial Intelligence, Priyanka Ramesh, Ramanathan Karuppasamy, Shanthi Veerappapillai

BioMedicine

Artificial intelligence has illustrated drastic changes in radiology and medical imaging techniques which in turn led to tremendous changes in screening patterns. In particular, advancements in these techniques led to the development of computer aided detection (CAD) strategy. These approaches provided highly accurate diagnostic reports which served as a “second-opinion” to the radiologists. However, with significant advancements in artificial intelligence strategy, the diagnostic and classifying capabilities of CAD system are meeting the levels of radiologists and clinicians. Thus, it shifts the CAD system from second opinion approach to a high utility tool. This article reviews the strategies and algorithms developed …