scholarly journals Testing the Static-99R as a Global Screen for Risk of Sex Crime Recidivism in a Norwegian Routine Sample

Sexual Abuse ◽  
2020 ◽  
pp. 107906322095119
Author(s):  
Ingeborg Jenssen Sandbukt ◽  
Torbjørn Skardhamar ◽  
Ragnar Kristoffersen ◽  
Christine Friestad
Keyword(s):  
Risk Assessment ◽  
Logistic Regression ◽  
Random Forest ◽  
Predictive Accuracy ◽  
Proportional Hazard ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Sex Offense ◽  
Observation Period ◽  
Sex Crime

The Static-99R has been recommended for use as a first global screen for sorting out sex-convicted persons who are in need of further risk assessment. This study investigated the Static-99R’s predictive validity based on a nonselected Norwegian sample ( n = 858) of persons released from prison after having served a sex crime sentence. After a mean observation period of 2,183 days, 3.4% ( n = 29) had recidivated to a new sex offense. A higher number of recidivists were found among those with higher Static-99R total scores. The predictive contribution from each of the ten Static-99R risk items was investigated using standard logistic regression, proportional hazard regression, and random forest classification algorithm. The overall results indicate that the Static-99R is relevant as a risk screen in a Norwegian context, providing similar results concerning predictive accuracy as previous studies.

A Hybrid Approach for Heart Disease Prediction

2021 ◽  
pp. 95-101
Author(s):  
Aamir Khan ◽  
Dr. Sanjay Jain
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Heart Disease ◽  
Random Forest ◽  
Research Work ◽  
Hybrid Approach ◽  
Disease Prediction ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Analysis Of Performance

The data mining (DM) is a process that deals with mining of valuable information from the rough data. The method of prediction analysis (PA) is implemented for predicting the future possibilities on the basis of current information. This research work is planned on the basis of predicting the heart disease. The coronary disorder can be forecasted in different phases in which pre-processing is done, attributes are extracted and classification is performed. The hybrid method is introduced on the basis of RF and LR.The Random Forest classification is adopted to extract the attributes and the classification process is carried out using logistic regression. The analysis of performance of introduced system is done with regard to accuracy, precision and recall. It is indicated that the introduced system will be provided accuracy approximately above 90% while predicting the heart disease.

scholarly journals Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification

2016 ◽  
Vol 146 ◽  
pp. 370-385 ◽  
Author(s):  
Adam Hedberg-Buenz ◽  
Mark A. Christopher ◽  
Carly J. Lewis ◽  
Kimberly A. Fernandes ◽  
Laura M. Dutca ◽  
...  
Keyword(s):  
Random Forest ◽  
Ganglion Cell ◽  
Retinal Ganglion Cell ◽  
Quantitative Measurement ◽  
Cell Populations ◽  
Retinal Ganglion ◽  
Random Forest Classification ◽  
Forest Classification
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