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Glaucoma is a serious eye disease characterized by dysfunction and loss of retinal ganglion cells (RGCs) which can eventually lead to loss of vision. Robust mass screening may help to extend the symptom-free life for the affected patients. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry (SLP), and Heidelberg Retina Tomography (HRT) scanning methods which, unfortunately, are expensive methods and hence, a novel automated glaucoma diagnosis system is needed. This paper proposes a new model for mass screening that aims to decrease the false negative rate (FNR). The model is based on applying nine different machine learning techniques in a majority voting model. The top five techniques that provide the highest accuracy will be used to build a consensus ensemble to make the final decision. The results from applying both models on a dataset with 499 records show a decrease in the accuracy rate from 90% to 83% and a decrease in false negative rate (FNR) from 8% to 0% for majority voting and consensus model, respectively. These results indicate that the proposed model can reduce FNR dramatically while maintaining a reasonable overall accuracy which makes it suitable for mass screening. [ABSTRACT FROM AUTHOR]

Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts. [ABSTRACT FROM AUTHOR]