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Regression Analysis

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Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning.

Medical physics
PURPOSE: The dosimetric accuracies of volumetric modulated arc therapy (VMAT) plans were predicted using plan complexity parameters via machine learning.

Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression.

Journal of medical systems
Nowadays, the most frequent cancer in women is breast cancer (malignant tumor). If breast cancer is detected at the beginning stage, it can often be cured. Many researchers proposed numerous methods for early prediction of this Cancer. In this paper,...

Cancer classification and pathway discovery using non-negative matrix factorization.

Journal of biomedical informatics
OBJECTIVES: Extracting genetic information from a full range of sequencing data is important for understanding disease. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type.

Automatic evaluation of fetal head biometry from ultrasound images using machine learning.

Physiological measurement
OBJECTIVE: Ultrasound-based fetal biometric measurements, such as head circumference (HC) and biparietal diameter (BPD), are frequently used to evaluate gestational age and diagnose fetal central nervous system pathology. Because manual measurements ...

Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance.

Computational and mathematical methods in medicine
Hepatitis B surface antigen (HBsAg) seroclearance during treatment is associated with a better prognosis among patients with chronic hepatitis B (CHB). Significant gaps remain in our understanding on how to predict HBsAg seroclearance accurately and ...

Model of a Support Vector Machine to Assess the Functional Cure for Surgery of Intermittent Exotropia.

Scientific reports
In this paper the optimum timing for the postoperative functional cure of basic intermittent exotropia is explored based on support vector machine (SVM). One hundred and thirty-two patients were recruited in this prospective cross-sectional study wit...

The application of machine learning methods for prediction of metal sorption onto biochars.

Journal of hazardous materials
The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44 biochars were modeled using artificial neural network (ANN) and random forest (RF) based on 353 dataset of adsorption experiments from literatures. The regres...

Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction.

PloS one
Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimizat...

An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective,...

Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.

Environmental monitoring and assessment
Rivers, as the most prominent component of water resources, have a key role to play in increasing the life expectancy of living creatures. The essential characteristics of water pollutants can be described by water quality indices (WQIs). Hence, a fe...