Logical Analysis of Data (LAD) model for the early diagnosis of acute ischemic stroke

<p>Abstract</p> <p>Background</p> <p>Strokes are a leading cause of morbidity and the first cause of adult disability in the United States. Currently, no biomarkers are being used clinically to diagnose acute ischemic stroke. A diagnostic test using a blood sample from a patient would potentially be beneficial in treating the disease.</p> <p>Results</p> <p>A classification approach is described for differentiating between proteomic samples of stroke patients and controls, and a second novel predictive model is developed for predicting the severity of stroke as measured by the National Institutes of Health Stroke Scale (NIHSS). The models were constructed by applying the Logical Analysis of Data (LAD) methodology to the mass peak profiles of 48 stroke patients and 32 controls. The classification model was shown to have an accuracy of 75% when tested on an independent validation set of 35 stroke patients and 25 controls, while the predictive model exhibited superior performance when compared to alternative algorithms. In spite of their high accuracy, both models are extremely simple and were developed using a common set consisting of only 3 peaks.</p> <p>Conclusion</p> <p>We have successfully identified 3 biomarkers that can detect ischemic stroke with an accuracy of 75%. The performance of the classification model on the validation set and on cross-validation does not deteriorate significantly when compared to that on the training set, indicating the robustness of the model. As in the case of the LAD classification model, the results of the predictive model validate the function constructed on our support-set for approximating the severity scores of stroke patients. The correlation and root mean absolute error of the LAD predictive model are consistently superior to those of the other algorithms used (Support vector machines, C4.5 decision trees, Logistic regression and Multilayer perceptron).</p

Linear predictive coding and its decision logic for early prediction of major adverse cardiac events using mass spectrometry data

Proteomics is an emerging field of modern biotechnology and an attractive research area in bioinformatics. Protein annotation by mass spectrometry has recently been utilized for the classification and prediction of diseases. In this paper we apply the theory of linear predictive coding and its decision logic for the prediction of major adverse cardiac risk using mass spectra. The new method was tested with a small set of mass spectrometry data. The initial experimental results are found promising for the prediction and show the implication of the potential use of the data for biomarker discovery.\ud \u