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Purpose This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings. Design/methodology/approach This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics. Findings Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting. Research limitations/implications While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK. Practical implications This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system. Originality/value This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

e13556 Background: Immune checkpoint inhibitors (ICI) are used to manage patients with both small cell (SCLC) and non-small cell (NSCLC) lung cancer. However, ICI response rates are often low, and identifying patients that will benefit from ICIs can be challenging. The value of biomarkers used to predict ICI response, such as PD-L1, Combined Positive Score (CPS) or tumor mutational burden (TMB) have been debated. Furthermore, the resources needed to assess these biomarkers may not be available in many centres. Developing more accurate and accessible tools that predict ICI responses could enable a precision medicine approach that improves patient outcomes. This study aimed to use a novel machine-learning (ML) algorithm to predict response to different ICI therapies in patients with lung cancer based on clinically available data. Methods: 334 eligible records were cleaned and reprocessed from textual to categorical data using one hot encoding. Complete datasets were available for 161 patients. Differences in the data distribution were handled using the Synthetic Minority Oversampling Technique. Six ML algorithms were trained, including Linear regression, Support Vector Classifier, XGBoost Classifier, Random Forest, Decision Tree, and Gaussian Naive Bayes Classifier. These algorithms used 80% of the training data, were tested on 20% of validation data and used the Grid Search Cross-Validation technique for hyperparameter optimization. Results: For the 161 patients included in the final analysis, the mean age was 68 years and 48% were female. 9% of patients had SCLC and 80% had NSCLC. Patients receiving Pembrolizumab, Nivolumab and Atezolizumab comprised 62%, 11% and 25%, respectively. The artificial intelligence (AI) algorithm predicted and stratified ICI response better than PD-L1 levels. Of the ML algorithms, XGBoost Classifier predicted response with the most accuracy, 64% (0.61 F1 score). This model found that good performance status (0-1), female gender and adenocarcinoma sub-type predicted response to ICI. On the other hand, M1, N2 staging, male gender, squamous cell carcinoma sub-type and receiving Atezolizumab were predictive of disease progression. Conclusions: This study developed multiple novel ML models to predict responses to ICIs in lung cancer. XGBoost Classifier used clinically available data to show that the type of ICI a patient receives, their histopathology sub-type and their TMN staging impact ICI response. Future work will aim to improve accuracy and predict ICI toxicity by including data from multiple centres, different cancer types and additional clinical variables. [Table: see text]

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