Comparative Analysis of Bitcoin Price Prediction Models: A Systematic literature review

An interpretable hybrid machine learning approach for predicting three-month unfavorable outcomes in patients with acute ischemic stroke.

EvaluateXAI: A framework to evaluate the reliability and consistency of rule-based XAI techniques for software analytics tasks

BackgroundLow birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse outcomes. Previous studies have proposed various machine learning (ML) models for LBW prediction task, but they were limited by small and imbalanced data sets. Some authors attempted to address this through different data rebalancing methods. However, most of their reported performances did not reflect the models’ actual performance in real-life scenarios. To date, few studies have successfully benchmarked the performance of ML models in maternal health; thus, it is critical to establish benchmarks to advance ML use to subsequently improve birth outcomes.ObjectiveThis study aimed to establish several key benchmarking ML models to predict LBW and systematically apply different rebalancing optimization methods to a large-scale and extremely imbalanced all-payer hospital record data set that connects mother and baby data at a state level in the United States. We also performed feature importance analysis to identify the most contributing features in the LBW classification task, which can aid in targeted intervention.MethodsOur large data set consisted of 266,687 birth records across 6 years, and 8.63% (n=23,019) of records were labeled as LBW. To set up benchmarking ML models to predict LBW, we applied 7 classic ML models (ie, logistic regression, naive Bayes, random forest, extreme gradient boosting, adaptive boosting, multilayer perceptron, and sequential artificial neural network) while using 4 different data rebalancing methods: random undersampling, random oversampling, synthetic minority oversampling technique, and weight rebalancing. Owing to ethical considerations, in addition to ML evaluation metrics, we primarily used recall to evaluate model performance, indicating the number of correctly predicted LBW cases out of all actual LBW cases, as false negative health care outcomes could be fatal. We further analyzed feature importance to explore the degree to which each feature contributed to ML model prediction among our best-performing models.ResultsWe found that extreme gradient boosting achieved the highest recall score—0.70—using the weight rebalancing method. Our results showed that various data rebalancing methods improved the prediction performance of the LBW group substantially. From the feature importance analysis, maternal race, age, payment source, sum of predelivery emergency department and inpatient hospitalizations, predelivery disease profile, and different social vulnerability index components were important risk factors associated with LBW.ConclusionsOur findings establish useful ML benchmarks to improve birth outcomes in the maternal health domain. They are informative to identify the minority class (ie, LBW) based on an extremely imbalanced data set, which may guide the development of personalized LBW early prevention, clinical interventions, and statewide maternal and infant health policy changes.

Three-level evaluation method of cumulative slope deformation hybrid machine learning models and interpretability analysis

The 7-point checklist (7PCL) is recommended by NICE to select patients with pigmented lesions and possible melanoma for urgent referral. This research investigates the potential of using machine learning (ML) models that utilize patient metadata from a teledermatology pathway including 7PCL for suspicious skin lesion detection, and compares the performance gain for each meta-feature added with the 7PCL. We analysed clinical metadata from 53 601 skin lesions in 25 105 patients who attended private skin cancer diagnosis clinics in the UK between 2015 and 2022. For each lesion, we included the following meta-features: 7PCL (change of lesion size, shape, colour, lesion > 7 mm, inflamed, oozing, itching), weighted 7PCL, Williams score characteristics (patient age, patient sex, natural hair colour, arm mole count, sunburn history, prior nonmelanoma skin cancer), the overall Williams score, prior melanoma, lesion location, lesion age, and whether this was a predominantly nonpigmented pink lesion. All lesions were categorized as suspicious (10%) or nonsuspicious (90%) by skin cancer specialists during telemedicine triage (cancer detection rate = 5%). All meta-features were used as ML model inputs to classify whether each input was associated with suspicious or nonsuspicious classes. This study integrates five ML models (naive Bayes, logistics regression, support vector machine, random forest, and multilayer perceptron) to detect suspicious skin lesions based on patient metadata. Firstly, we compared the ML model performance between the original 7PCL and weighted 7PCL in correctly identifying suspicious skin lesions. We then added each meta-feature with 7PCL and tabulated the performance gain. We utilized balanced accuracy, sensitivity, specificity and area under the curve as evaluation metrics. There were no significant differences observed in model performance between the 7PCL and weighted 7PCL (sensitivity 68.1%). However, the performance of the ML model was improved significantly for each added meta-feature, with the best performance gain (sensitivity 85.2%) observed when 11 meta-features (lesion pink, Williams score, lesion age, sunburn, patient age, Williams group, patient sex, hair colour, site of the lesion on the body, freckling tendency, and mole count) were added to the 7PCL. This research has identified the optimal subset of meta-features that improve ML model performance for categorizing suspicious skin lesions during telemedicine triage, compared with 7PCL alone. Fusing these high-performing meta-features with image modalities is likely to further boost the ML model performance for skin cancer detection, and they could also be used to modify current skin cancer referral guidelines. The study is funded and supported by Innovate UK and the private teledermatology pathway provider.

The 7-point checklist (7PCL) is recommended by NICE to select patients with pigmented lesions and possible melanoma for urgent referral. This research investigates the potential of using machine learning (ML) models that utilize patient metadata from a teledermatology pathway including 7PCL for suspicious skin lesion detection, and compares the performance gain for each meta-feature added with the 7PCL. We analysed clinical metadata from 53 601 skin lesions in 25 105 patients who attended private skin cancer diagnosis clinics in the UK between 2015 and 2022. For each lesion, we included the following meta-features: 7PCL (change of lesion size, shape, colour, lesion > 7 mm, inflamed, oozing, itching), weighted 7PCL, Williams score characteristics (patient age, patient sex, natural hair colour, arm mole count, sunburn history, prior nonmelanoma skin cancer), the overall Williams score, prior melanoma, lesion location, lesion age, and whether this was a predominantly nonpigmented pink lesion. All lesions were categorized as suspicious (10%) or nonsuspicious (90%) by skin cancer specialists during telemedicine triage (cancer detection rate = 5%). All meta-features were used as ML model inputs to classify whether each input was associated with suspicious or nonsuspicious classes. This study integrates five ML models (naive Bayes, logistics regression, support vector machine, random forest, and multilayer perceptron) to detect suspicious skin lesions based on patient metadata. Firstly, we compared the ML model performance between the original 7PCL and weighted 7PCL in correctly identifying suspicious skin lesions. We then added each meta-feature with 7PCL and tabulated the performance gain. We utilized balanced accuracy, sensitivity, specificity and area under the curve as evaluation metrics. There were no significant differences observed in model performance between the 7PCL and weighted 7PCL (sensitivity 68.1%). However, the performance of the ML model was improved significantly for each added meta-feature, with the best performance gain (sensitivity 85.2%) observed when 11 meta-features (lesion pink, Williams score, lesion age, sunburn, patient age, Williams group, patient sex, hair colour, site of the lesion on the body, freckling tendency, and mole count) were added to the 7PCL. This research has identified the optimal subset of meta-features that improve ML model performance for categorizing suspicious skin lesions during telemedicine triage, compared with 7PCL alone. Fusing these high-performing meta-features with image modalities is likely to further boost the ML model performance for skin cancer detection, and they could also be used to modify current skin cancer referral guidelines. The study is funded and supported by Innovate UK and the private teledermatology pathway provider.

The increasing prevalence of diabetes necessitates the development of effective early detection methods to mitigate its health impacts. This paper investigates the impact of feature transformation and machine learning (ML) models on the early detection of diabetes using a binary tabular classification dataset. We explore three feature transformation techniques, no transformation, normalization, and min-max scaling, to assess their influence on the performance of various ML models. To comprehensively evaluate the effectiveness of these preprocessing techniques, we experimented with twelve different ML models, including both traditional algorithms and ensemble methods. A publicly available dataset has been used for this research, containing 768 samples and 8 features. To ensure their effectiveness, the models are assessed using several evaluation metrics, including accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Among the ML models, Light Gradient Boosting Machine (LGBM) achieved the highest accuracy of 82.91% when min-max scaling was applied to the data. Our results demonstrate the varying effectiveness of different combinations of feature transformation techniques and ML models in enhancing diabetes detection performance. Furthermore, it has been observed that the ensemble models generally achieved better performance than traditional ML models. These findings provide valuable insights for optimizing preprocessing and model selection strategies in the development of robust early diabetes detection systems.

Microorganisms growing in their habitat constitute a complex system. How the individual constituents of the environment contribute to microbial growth remains largely unknown. The present study focused on the contribution of environmental constituents to population dynamics via a high-throughput assay and data-driven analysis of a wild-type Escherichia coli strain. A large dataset constituting a total of 12,828 bacterial growth curves with 966 medium combinations, which were composed of 44 pure chemical compounds, was acquired. Machine learning analysis of the big data relating the growth parameters to the medium combinations revealed that the decision-making components for bacterial growth were distinct among various growth phases, e.g., glucose, sulfate, and serine for maximum growth, growth rate, and growth delay, respectively. Further analyses and simulations indicated that branched-chain amino acids functioned as global coordinators for population dynamics, as well as a survival strategy of risk diversification to prevent the bacterial population from undergoing extinction.

The cryptocurrency market is one of the most dynamic and volatile markets in the world's financial ecosystem, and investment landscapes in the US financial market have changed so much. In slightly over a decade, cryptocurrencies have moved from niche digital assets to mainstream investment opportunities such as Bitcoin, Ethereum, and many others. The prime objective of this research project was to investigate the effectiveness of various machine learning algorithms in the prediction of cryptocurrency prices within the volatile US financial market. This research pinpointed which Machine Learning techniques provide the most accurate and reliable predictions under different market conditions, with a full understanding of their strengths and limitations. The dataset gathered for analyzing and forecasting cryptocurrency prices entailed diverse and extensive data points, affirming a well-rounded foundation for machine learning algorithms. Particularly, current and historic price data from cryptocurrency exchanges such as Binance, Coinbase, and Kraken, together with trading metrics important for the definition of market dynamics. Aggregated data from financial databases such as Coin-Market-Cap, Crypto-Compare, and Yahoo Finance comes in structured form and presents historical consistency, hence perfectly fitting for machine learning applications. Models considered for the study ranged from simple, linear methods to complex ensemble and gradient-boosting algorithms. Precise performance evaluation is a proxy of its reliability and correctness of effectiveness in price predictions in a cryptocurrency market. Several measures of the effectiveness of prediction have been used here for assessing the different properties of models' performance: Precision, Recall, and F1-Score. Additional performance metrics were applied to evaluate the models in this study including Mean Absolute Error, Root Mean Squared Error, and R-squared. The gradient Boosting model did an excellent job as compared to other algorithms, as the values of accuracy, precision, recall, and F1-score for both classes were quite high. All three models have quite a relatively low MAE and RMSE, which means that each model is remarkably good at predicting the target variable. The application of machine learning models in the sphere of cryptocurrency price prediction might finally give very important implications to investors and stakeholders of the financial market in the USA, especially since recently, cryptocurrencies have been made integral parts of both individual and institutional investors' portfolios and trading strategies. To investors, it may provide indications of the entry and exit points, diversification of portfolios, and risk management by using machine learning models. Consolidation with the financial system will indeed mark a strategic shift toward data-driven decision-making in investment management and trading by integrating machine learning models into the financial systems.

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

AI Predicts Early Relapse Post-Axicabtagene Ciloleucel in Diffuse Large B-Cell Lymphoma Patients in a Multi-Center Real-World Study

Given that aging deterioration significantly influences the structural behavior of reinforced concrete (RC) nuclear power plant (NPP) structures, it is crucial to incorporate changes in the material properties of NPPs for accurate prediction of seismic responses. In this study, machine learning (ML) models for predicting the seismic response of RC NPP structures were developed by considering aging deterioration. The OPR1000 was selected as a representative structure, and its finite element model was generated. A total of 500 artificial ground motions were created for time history analyses, and the analytical results were utilized to establish a database for training and testing ML models. Six ML algorithms, commonly employed in the structural engineering domain, were used to construct the seismic response prediction model. Thirteen intensity measures of artificial earthquakes and four material properties were employed as input parameters for the training database. The floor response spectrum of the example structure was chosen as the output for the database. Four evaluation metrics were implemented as quantitative measures to assess the prediction performance of the ML models. This study used multiple input variables to represent the characteristics of the seismic loads and changes in material properties, thereby increasing the minimum required database size for ML model development. This increase may extend the time and effort required to construct the database. Consequently, this study also explored the possibility of reducing the minimum required database size and the prediction performance through input dimension reduction of the ML model. Numerical results demonstrated that the developed ML model could effectively predict the seismic responses of RC NPP structures, taking into account aging deterioration.

Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interpretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance (area under the precision recall curve = 0.83) achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis.

Emotion recognition from textual data has become increasingly vital in domains such as sentiment-aware systems, conversational agents, and mental health analysis. Despite significant progress, accurately detecting emotions from text remains a challenging task due to the lack of prosodic and visual cues, contextual ambiguity, and imbalanced datasets. This study presents a comprehensive evaluation of traditional Machine Learning (ML) and advanced Deep Learning (DL) models on four diverse emotion-labeled datasets: DailyDialog, ISEAR, Emotion-Stimulus, and CrowdFlower. Various feature extraction techniques—TF-IDF and Count Vectorizer for ML models, and semantic embeddings (Word2Vec and GloVe) for DL models—were employed to assess their impact on model performance. The models compared include Logistic Regression, Random Forest, Stochastic Gradient Descent, and Multinomial Naïve Bayes for ML, and LSTM, BiLSTM, and CNN for DL. Evaluation metrics such as accuracy, precision, recall, F1-score, and MCC were used for performance assessment. Results reveal that DL models, particularly CNN and BiLSTM, outperform ML models in terms of accuracy and contextual understanding, especially on structured datasets. Conversely, Logistic Regression with TF-IDF demonstrates robustness on noisy and imbalanced data. Word2Vec embeddings consistently enhance DL model performance, highlighting the importance of contextual semantics. This work underscores the significance of dataset characteristics, model architecture, and feature representation in achieving effective emotion classification. Future directions include integrating transformer-based models, addressing class imbalance, and exploring multimodal emotion recognition to improve generalization and real-world applicability.

Fracturing in horizontal wells influenced by high tectonic effects is challenging in terms of achieving rock breakdown and fracture propagation. Near-wellbore complexities also lead to insufficient injection rate, post-breakdown, to place proppant. A machine-learning (ML) model based on in-depth multidomain analysis can assist in such cases in the design and execution phase. Part I of the paper here covers the extensive ML modeling. The following Part II will cover the full implementation scheme applied on full well logs and complete data. A total of 106 fracturing stages were analyzed across 12 wells with a structured database created with 52 fracturing-relevant parameters. The dataset for ML modeling was skimmed down to 24 inputs and 4 output parameters. These included different phases of the well, such as drilling and completion, processed openhole logs, perforation details, fracturing treatment parameters, and pressure diagnostics data. A placement quality index (PQI) was calculated with mass of proppant placed, rate achieved, pressures experienced, etc. with application of appropriate weights on each. The PQI used weighting techniques such as the analytic hierarchy process and entropy weight method. Multiple classification and regression algorithms were tested and used to learn from these inputs to predict stage placement and proppant placement success. An algorithm comparison was done to select the best performing algorithms for each of the different prediction tasks. A detailed data exploration, feature engineering, and data preprocessing was conducted to study the correlations, establish causality, scale the data and prepare it to train/test the models. The proposed ML workflow in the study consists of a three-step process: (1) a classification model used to predict stage skipped, which is crucial as it influences the subsequent regression models. Results showed an excellent result in the predictions with an accuracy of 94%. (2) Multiple regression models were implemented to predict injectivity index, total proppant, proppant load, and the PQI. Predictions were evaluated using several evaluation metrics including R2 (varying from 0.86 to 0.93), root mean square error (RMSE), and mean absolute error (MAE). Results showed a good performance that varied across the different models. (3) A particle swarm optimizer algorithm was used downstream to optimize the perforation and treatment design to enhance the success ratio based on PQI prediction. The algorithm aimed to maximize the PQI by varying the parameters in the search space within reasonable and practicable ranges that was divided by completion type. Results showed an enhancement of 93% and 63% on low PQI section; 8% and 11% on mean values, for cased hole completion and for openhole completion, respectively. This work is a first attempt to use ML in enhancing proppant placement. This approach can be used with the existing reservoir quality, completion quality, and geologic quality indices to append the understanding and design of treatments and perforations. The deployment plan will be conducted into existing commercial numerical models to assist the engineers during the design process.