Optimization-based predictive models in medicine and biology

Analysis of the consistency of a mixed integer programming-based multi-category constrained discriminant model

“…In addition to heart disease and thyroid disease classification, DAMIP has been applied successfully to prediction of ultrasonic cell disruption for drug delivery (Lee et al 2004), genomic analysis and prediction of aberrant CpG island methylation in human cancer (Feltus et al 2003(Feltus et al , 2006, and identification of tumor shape and volume in treatment of sarcoma (Lee et al 2002) applications. Further, Lee (2007aLee ( , 2007b, Lee and Wu (2007) demonstrated the classification capability of DAMIP over a wide variety of medical and biological problems in which DAMIP offers superior classification results (both in ten-fold cross-validation as well as in blind tests) when compared to other classification approaches.…”

Large-Scale Optimization-Based Classification Models in Medicine and Biology

“…14,33,49,50,56 Our models seek to maximize the correct classification rate while constraining the number of misclassifications in each group. The models incorporate the following features: (1) the ability to classify any number of distinct groups; (2) allow incorporation of heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) constraining the misclassification in each group and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resul ting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reservedjudgment region.…”

“…Utilizing the technology of large-scale discrete optimization and support-vector machines, we have developed novel predictive models that simultaneously include the following features: (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reservedjudgment region. Based on the description in Gallagher et al, 32,33 Lee et al, 56 and Lee, 49,50 we summarize below some of the classification models we have developed.…”

“…Extensive computational experiments have been performed to gauge the qualities of solutions of different models. 14,33,49,50,56 Computational Strategies…”

“…Our model, the first discrete support-vector machine, offers distinct features (e.g., the ability to classify any number of groups, management of the curse of dimensionality in data attributes, and a reserved-judgment region to facilitate multi-stage classification analysis) that are not simultaneously available in existing classification software. 32,33,49,50,56 Studies involving tumor volume identification, ultrasonic cell disruption in drug delivery, lung tumor cell motility analysis, CpG island aberrant methylation in human cancer, predicting early atherosclerosis using biomarkers, and fingerprinting native and angiogenic microvascular networks using functional perfusion data indicate that our zapproach is adaptable and can produce effective and reliable predictive rules for various biomedical and bio-behavior phenomena. 13,25,26,51,53,55,57 , Section ''Background'' briefly describes the background of discriminant analysis.…”