Background: Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. Methods: A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan–Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. Results: We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. Conclusion: The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.

Background: Blood component transfusions are a common and often necessary medical practice during the epidemics of dengue. Transfusions are required for patients when they developed severe dengue fever or thrombocytopenia of 10×109/L or less. This study therefore investigated the risk factors, performance and effectiveness of eight different machine-learning algorithms to predict blood component transfusion requirements in confirmed dengue cases admitted to hospital. The objective was to study the risk factors that can help to predict blood component transfusion needs. Methods: Eight predictive models were developed based on retrospective data from a private group of hospitals in India. A python package SHAP (SHapley Additive exPlanations) was used to explain the output of the “XGBoost” model. Results: Sixteen vital variables were finally selected as having the most significant effects on blood component transfusion prediction. The XGBoost model presented significantly better predictive performance (area under the curve: 0.793; 95 % confidence interval: 0.699–0.795) than the other models. Conclusion: Predictive modelling techniques can be utilized to streamline blood component preparation procedures and can help in the triage of high-risk patients and readiness of caregivers to provide blood component transfusions when required. This study demonstrates the potential of multilayer algorithms to reasonably predict any blood component transfusion needs which may help healthcare providers make more informed decisions regarding patient care.