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• W2508925057 abstract "We present novel optimization-based classification models that are of general purpose and suitable for establishing reliable predictive rules for a broad variety of biological and medical applications. Our predictive framework simultaneously incorporates (i) the ability to classify any number of distinct groups; (ii) the ability to incorporate heterogeneous and temporal types of attributes as input; (iii) a high-dimensional data transformation that minimizes noise and errors; (iv) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against overtraining (which tends to lead to high misclassification rates from the resulting predictive rule); and (v) successive multistage classification capability to handle data points placed in the reserved-judgment region. To illustrate the power and flexibility of the classification model and solution engine and its multigroup prediction capability, applications of the predictive model to a broad class of biological and medical problems are described. Applications include vaccine immunity prediction; early detection of mild cognitive impairment (MCI) and Alzheimer's disease (AD); predicting aberrant CpG island methylation in human; predicting ultrasonic cell disruption for drug delivery; uncovering tumor shape and volume in the treatment of sarcoma; multistage discriminant analysis of biomarkers for detecting early atherosclerosis; and fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy, and tumor metastasis. Furthermore, we also demonstrate the classification performance on instances from the UCI Repository of machine learning databases. In all these applications, the classification rules yield blind prediction accuracy ranging from 80% to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring, and decision-making tool." @default.
• W2508925057 created "2016-09-16" @default.
• W2508925057 creator A5050619335 @default.
• W2508925057 date "2016-08-12" @default.
• W2508925057 modified "2023-10-12" @default.
• W2508925057 title "Predictive Analytics: Classification in Medicine and Biology" @default.
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• W2508925057 doi "https://doi.org/10.1002/9781118919408.ch6" @default.
• W2508925057 hasPublicationYear "2016" @default.
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