AIMC Topic: Machine Learning

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IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning.

Computational intelligence and neuroscience
A genetic disorder is a serious disease that affects a large number of individuals around the world. There are various types of genetic illnesses, however, we focus on mitochondrial and multifactorial genetic disorders for prediction. Genetic illness...

The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric.

BioMed research international
Pneumonia is a common lung disease that is the leading cause of death worldwide. It primarily affects children, accounting for 18% of all deaths in children under the age of five, the elderly, and patients with other diseases. There is a variety of i...

Predicting age and gender from network telemetry: Implications for privacy and impact on policy.

PloS one
The systematic monitoring of private communications through the use of information technology pervades the digital age. One result of this is the potential availability of vast amount of data tracking the characteristics of mobile network users. Such...

Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks.

PloS one
BACKGROUND: Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to t...

The Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis.

PloS one
This paper dissects the potential of state-of-the-art computational analysis to promote the investigation of government's administrative decisions and politics. The Executive Branch generates massive amounts of textual data comprising daily decisions...

Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure.

International journal of cardiology
BACKGROUND: Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and...

A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation.

Kidney360
BACKGROUND: The first 90 days after dialysis initiation are associated with high morbidity and mortality in end-stage kidney disease (ESKD) patients. A machine learning-based tool for predicting mortality could inform patient-clinician shared decisio...

Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space.

Journal of chemical theory and computation
We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an ent...

Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility-High-Resolution Mass Spectrometry and in Silico Prediction Tools.

Journal of agricultural and food chemistry
The identification of migrates from food contact materials (FCMs) is challenging due to the complex matrices and limited availability of commercial standards. The use of machine-learning-based prediction tools can help in the identification of such c...

Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework.

Sensors (Basel, Switzerland)
Although increasing evidences support the notion that psychiatric disorders are associated with abnormal communication between brain regions, scattered studies have investigated brain electrophysiological disconnectivity of patients with generalized ...