Application of machine learning in predicting non-alcoholic fatty liver disease using anthropometric and body composition indices.

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, which can progress from simple steatosis to advanced cirrhosis and hepatocellular carcinoma. Clinical diagnosis of NAFLD is crucial in the early stages of the disease. The main aim of this study was to apply machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables. A cross-sectional study was carried out among 513 individuals aged 13 years old or above in Iran. Anthropometric and body composition measurements were performed manually using body composition analyzer InBody 270. Hepatic steatosis and fibrosis were determined using a Fibroscan. ML methods including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease. RF generated the most accurate model for fatty liver (presence of any stage), steatosis stages and fibrosis stages with 82%, 52% and 57% accuracy, respectively. Abdomen circumference, waist circumference, chest circumference, trunk fat and body mass index were among the most important variables contributing to fatty liver disease. ML-based prediction of NAFLD using anthropometric and body composition data can assist clinicians in decision making. ML-based systems provide opportunities for NAFLD screening and early diagnosis, especially in population-level and remote areas.

Diagnosis of autism spectrum disorder based on complex network features.

BACKGROUND AND OBJECTIVES: Autism spectrum disorder (ASD) is a disorder in the information flow of the human brain system that can lead to secondary problems for the patient. Only when ASD is diagnosed by clinical methods can the secondary problems be detected. Hence, diagnosis of ASD at an early age and in young children can prevent its secondary effects. METHODS: By employing the visibility graph (VG) algorithm, the present study examines the C3 single-channel of EEG signals and presents the differences among the topological features of complex networks resulting from these signals. The average degree (AD) can be a method for the detection of normal and ASD samples. RESULTS: With an accuracy 81/67%, the ASD class can be discerned. CONCLUSIONS: The current paper demonstrates that only by the usage of EEG signals of the brain's C3 channel and the topological features of its network (AD and related features, such as RADACC and RADMPL) can ASD and NC target classes be distinguished at an early age.