Fall detection system for elderly people using IoT and ensemble machine learning algorithm

Acute kidney injury (AKI) is a common postoperative complication among patients in the neurological intensive care unit (NICU), often resulting in poor prognosis and high mortality. In this retrospective cohort study, we established a model for predicting AKI following brain surgery based on an ensemble machine learning algorithm using data from 582 postoperative patients admitted to the NICU at the Dongyang People's Hospital from March 1, 2017, to January 31, 2020. Demographic, clinical, and intraoperative data were collected. Four machine learning algorithms (C5.0, support vector machine, Bayes, and XGBoost) were used to develop the ensemble algorithm. The AKI incidence in critically ill patients after brain surgery was 20.8%. Intraoperative blood pressure; postoperative oxygenation index; oxygen saturation; and creatinine, albumin, urea, and calcium levels were associated with the postoperative AKI occurrence. The area under the curve value for the ensembled model was 0.85. The accuracy, precision, specificity, recall, and balanced accuracy values were 0.81, 0.86, 0.44, 0.91, and 0.68, respectively, indicating good predictive ability. Ultimately, the models using perioperative variables exhibited good discriminatory ability for early prediction of postoperative AKI risk in patients admitted to the NICU. Thus, the ensemble machine learning algorithm may be a valuable tool for forecasting AKI.

This study looked at novel data sources for cardiovascular risk prediction including detailed lifestyle questionnaire and continuous blood pressure monitoring, using ensemble machine learning algorithms (MLAs). The reference conventional risk score compared against was the Framingham Risk Score (FRS). The outcome variables were low or high risk based on calcium score 0 or calcium score 100 and above. Ensemble MLAs were built based on naive bayes, random forest and support vector classifier for low risk and generalized linear regression, support vector regressor and stochastic gradient descent regressor for high risk categories. MLAs were trained on 600 Southeast Asians aged 21 to 69 years free of cardiovascular disease. All MLAs outperformed the FRS for low and high-risk categories. MLA based on lifestyle questionnaire only achieved AUC of 0.715 (95% CI 0.681, 0.750) and 0.710 (95% CI 0.653, 0.766) for low and high risk respectively. Combining all groups of risk factors (lifestyle survey questionnaires, clinical blood tests, 24-h ambulatory blood pressure and heart rate monitoring) along with feature selection, prediction of low and high CVD risk groups were further enhanced to 0.791 (95% CI 0.759, 0.822) and 0.790 (95% CI 0.745, 0.836). Besides conventional predictors, self-reported physical activity, average daily heart rate, awake blood pressure variability and percentage time in diastolic hypertension were important contributors to CVD risk classification.

Laboratory-based hyperspectral imaging (HSI) is an optical non-destructive technology used to extract mineralogical information from bedrock drill cores. In the present study, drill core scanning in the long-wave infrared (LWIR; 8000–12,000 nm) wavelength region was used to map the dominant minerals in HSI pixels. Machine learning classification algorithms, including random forest (RF) and support vector machine, have previously been applied to the mineral characterization of drill core hyperspectral data. The objectives of this study are to expand semi-automated mineral mapping by investigating the mapping accuracy, generalization potential, and classification ability of cutting-edge methods, such as various ensemble machine learning algorithms and deep learning semantic segmentation. In the present study, the mapping of quartz, talc, chlorite, and mixtures thereof in HSI data was performed using the ENVINet5 algorithm, which is based on the U-net deep learning network and four decision tree ensemble algorithms, including RF, gradient-boosting decision tree (GBDT), light gradient-boosting machine (LightGBM), AdaBoost, and bagging. Prior to training the classification models, endmember selection was employed using the Sequential Maximum Angle Convex Cone endmember extraction method to prepare the samples used in the model training and evaluation of the classification results. The results show that the GBDT and LightGBM classifiers outperformed the other classification models with overall accuracies of 89.43% and 89.22%, respectively. The results of the other classifiers showed overall accuracies of 87.32%, 87.33%, 82.74%, and 78.32% for RF, bagging, ENVINet5, and AdaBoost, respectively. Therefore, the findings of this study confirm that the ensemble machine learning algorithms are efficient tools to analyze drill core HSI data and map dominant minerals. Moreover, the implementation of deep learning methods for mineral mapping from HSI drill core data should be further explored and adjusted.

P125. Development of a novel ensemble machine learning algorithm for prediction of complications and readmission after anterior cervical spinal fusion

This paper analyses the recent evolution of the electricity price of one of the EastEuropean countries’ Balancing Markets (BM) – Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants’ trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players.

Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km2 were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km2 at the 2 year return period and 40 km2 at the 100 year return period for the high landslide hazard zone, and 6 km2 at the 2 year return period and 20 km2 at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km2 to 62 km2). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.

A machine learning approach to solve a multiple regression problem is considered. Mass concentration of aerosol particles in the surface layer of the atmosphere was used as a dependent variable. The aerosol optical depth of the atmosphere and a number of meteorological parameters from the ECMWF ERA5 reanalysis database were chosen as predictors. The problem was solved using an ensemble machine learning algorithm - a random forest.

Warpage prediction of Injection-molded PVC part using ensemble machine learning algorithm

Accurate oil palm yield prediction is necessary to sustain oil palm production for food security and economic return. However, there are limited studies on comprehensive mapping and accurate oil palm yield prediction using advanced machine learning algorithms. Using multi-temporal remote sensing data, this paper proposed a new approach to predict oil palm yield based on the normalized difference vegetation index (NDVI) and ensemble machine learning algorithm. ReliefF algorithm with linear projection was employed to select the best combination of spectral indices in oil palm discrimination. Oil palm land cover was classified using random forest (RF) and modified AdaBoost algorithms. A time-series approach known as walk-forward validation was firstly introduced to train the model using the 2016-2019 data and the one-step prediction was performed for 2020 using RF and AdaBoost. Result of the study revealed that the RF model (RMSE = 0.384; MSE = 0.148; MAE = 0.147) outperformed the AdaBoost model (RMSE = 0.410; MSE = 0.168; MAE = 0.176). Our research has demonstrated the value of detailed mapping and subsequent yield prediction by developing a novel approach utilising time-series satellite imagery, ensemble machine learning, and NDVI, which will assist decision-makers in managing their practices related to oil palm.

Machine Learning is a branch of Artificial Intelligence that predicts several naturally occurring events by training a model with some data and then using unseen data to test it. This paper seeks to analyze the performances of single and ensemble machine learning algorithms on the Cleveland Heart disease data set. Experimental study proves that the accuracy score and area under the ROC curve in the ensemble machine learning model is higher than the single machine learning model in predicting non-CVD and CVD patients.

Ambulance dispatch prioritisation for traffic crashes using machine learning: A natural language approach

Elderly falls are a growing phenomenon observed within the world. According to World Health Organization (WHO), it is the second leading cause of unintentional or accidental deaths among the elderly. Thus, the need for research regarding the development of fall detection systems is imperative. Researchers have utilized various approaches to develop fall detection systems, significant number of which have employed Machine Leaning (ML) algorithms for fall detection. In this study, we evaluated the efficiency of six ML algorithms on a public fall detection dataset. A robust deep neural network for fall detection (FD-DNN) is identified to be the current state-of-the-art, it detects falls by using a self-built sensor that consumes low power. By evaluating the efficiency of six machine learning algorithms on a publicly available joint fall detection dataset, the accuracy of the fall detection was increased from 99.17% to 99.88% by using the K-nearest Neighbor indicating that common machine learning algorithms can achieve identical or higher accuracy rendering the complex and expensive deep neural network-based fall detection systems inefficient.KeywordsFall detectionElderly fall detectionMachine learning algorithmsWearable fall detection systemSisFall datasetMobiFall datasetK-nearest neighborsKNNSupport vector machinesSVMDecision treeRandom forestLogistic regressionNaïve Bayes

Falls pose a significant risk for the elderly population, often resulting in serious injuries and decreased quality of life. Accurate and effective fall detection systems can play an important role in reducing these risks. This study conducts a comparative analysis of the performance of PySpark and Scikit-Learn libraries in the development of fall detection models. PySpark offers a distributed computing environment specifically designed for big data processing, while Scikit-Learn is a Python-based library widely used for data analysis and machine learning. Five popular machine learning algorithms, including logistic regression, gradient boosting classifier, random forest, support vector machine, and Decision Tree, were used to build fall detection models using both PySpark and Scikit-Learn. Models were evaluated using a comprehensive set of measurements including accuracy, sensitivity, specificity, and confusion matrix. In addition to the five popular machine learning algorithms included in the study, 26 different features were extracted from the Sisfall dataset, one of the most comprehensive fall and daily life activity datasets, in five main categories: basic statistical features, frequency domain features, time series features, motion features, and relational features. These features were incorporated into fall detection models to improve their ability to accurately identify falls. The findings reveal that both PySpark and Scikit-Learn provide strong and effective results in fall detection. Logistic regression emerged as the best performing algorithm in both libraries, achieving the highest accuracy rates. However, PySpark exhibited slightly longer training times compared to Scikit-Learn, which performed better in testing. Ultimately, this study will benefit researchers and practitioners involved in the development of robust and effective fall detection systems, thereby enabling improved safety and well-being for the elderly population. Additionally, it contributes to the literature by presenting a comprehensive feature extraction methodology for fall detection, which has not been explored extensively in previous research works.

Forest aboveground carbon storage estimation and uncertainty analysis by coupled multi-source remote sensing data in Liaoning Province

Background: Cardiac arrest (CA) poses a significant risk of death and long-term neurological disability yet there remains a lack of accurate and reliable methods to prognosticate post-CA outcome, indicating a major unmet need. Additionally, no existing CA outcome prediction model utilizes physiological time series (PTS) data, which captures transitions in a patient’s health state at high temporal resolution. Materials and Methods: We screened the Philips eICU database, which includes over 200,000 patients in 200 hospitals, extracting data on CA patients within 24 hours of ICU admission. We first extracted PTS and Electronic Health Record (HER) features and selected our features using nested Random Forest. The outcome labels were neurological state or mortality at ICU discharge. We tested various individual and ensemble machine learning algorithms (GLM, Random Forest, Gradient Boosting, Support Vector Machine, Neural Networks) and optimization techniques (hyperparameter tuning, model stacking, and transfer learning) to maximize the accuracy of outcome prediction. Results: For prediction of neurological outcome, our model combining EHR and PTS features achieved a higher sensitivity (0.78), specificity (0.88), and AUC (0.87±0.01) compared to the APACHE baseline standard model (AUC: 0.74±0.01, sensitivity: 0.77, specificity: 0.63). Also, for prediction of mortality our model (AUC: 0.81±0.01, sensitivity: 0.78, specificity: 0.71) outperformed the APACHE clinical baseline (AUC: 0.75±0.01, sensitivity: 0.86, specificity: 0.56) by 7%. The integration of PTS with EHR data increased mean AUC by 4% for neurological outcome and 2% for mortality. Conclusion: Results demonstrate that for both neurological outcome and mortality prediction, our ensemble model was significantly more effective compared to the baseline APACHE model. Additionally, we find that PTS data adds to the discriminative power of clinical models. With appropriate validation in prospective cohorts, these findings could be used as point-of-care references to aid ICU physicians in clinical decision-making.