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Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing
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作者 Abraham Jallah Balyemah Sonkarlay J. Y. Weamie +2 位作者 Jiang Bin Karmue Vasco Jarnda Felix Jwakdak Joshua 《International Journal of Communications, Network and System Sciences》 2024年第6期81-103,共23页
This research introduces a novel approach to improve and optimize the predictive capacity of consumer purchase behaviors on e-commerce platforms. This study presented an introduction to the fundamental concepts of the... This research introduces a novel approach to improve and optimize the predictive capacity of consumer purchase behaviors on e-commerce platforms. This study presented an introduction to the fundamental concepts of the logistic regression algorithm. In addition, it analyzed user data obtained from an e-commerce platform. The original data were preprocessed, and a consumer purchase prediction model was developed for the e-commerce platform using the logistic regression method. The comparison study used the classic random forest approach, further enhanced by including the K-fold cross-validation method. Evaluation of the accuracy of the model’s classification was conducted using performance indicators that included the accuracy rate, the precision rate, the recall rate, and the F1 score. A visual examination determined the significance of the findings. The findings suggest that employing the logistic regression algorithm to forecast customer purchase behaviors on e-commerce platforms can improve the efficacy of the approach and yield more accurate predictions. This study serves as a valuable resource for improving the precision of forecasting customers’ purchase behaviors on e-commerce platforms. It has significant practical implications for optimizing the operational efficiency of e-commerce platforms. 展开更多
关键词 E-Commerce Platform Purchasing Behavior Prediction logistic Regression algorithm
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High Accuracy Gene Signature for Chemosensitivity Prediction in Breast Cancer
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作者 Wei Hu 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2015年第5期530-536,共7页
Neoadjuvant chemotherapy for breast cancer patients with large tumor size is a necessary treatment.After this treatment patients who achieve a pathologic Complete Response(p CR) usually have a favorable prognosis th... Neoadjuvant chemotherapy for breast cancer patients with large tumor size is a necessary treatment.After this treatment patients who achieve a pathologic Complete Response(p CR) usually have a favorable prognosis than those without. Therefore, p CR is now considered as the best prognosticator for patients with neoadjuvant chemotherapy. However, not all patients can benefit from this treatment. As a result, we need to find a way to predict what kind of patients can induce p CR. Various gene signatures of chemosensitivity in breast cancer have been identified, from which such predictors can be built. Nevertheless, many of them have their prediction accuracy around 80%. As such, identifying gene signatures that could be employed to build high accuracy predictors is a prerequisite for their clinical tests and applications. Furthermore, to elucidate the importance of each individual gene in a signature is another pressing need before such signature could be tested in clinical settings. In this study, Genetic Algorithm(GA) and Sparse Logistic Regression(SLR) along with t-test were employed to identify one signature. It had 28 probe sets selected by GA from the top 65 probe sets that were highly overexpressed between p CR and Residual Disease(RD) and was used to build an SLR predictor of p CR(SLR-28). This predictor tested on a training set(n = 81) and validation set(n = 52) had very precise predictions measured by accuracy,specificity, sensitivity, positive predictive value, and negative predictive value with their corresponding P value all zero. Furthermore, this predictor discovered 12 important genes in the 28 probe set signature. Our findings also demonstrated that the most discriminative genes measured by SLR as a group selected by GA were not necessarily those with the smallest P values by t-test as individual genes, highlighting the ability of GA to capture the interacting genes in p CR prediction as multivariate techniques. Our gene signature produced superior performance over a signature found in one previous study with prediction accuracy 92% vs 76%, demonstrating the potential of GA and SLR in identifying robust gene signatures in chemo response prediction in breast cancer. 展开更多
关键词 genetic algorithm gene signature breast cancer sparse logistic regression predictor chemosensitivity
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