Machine Learning Based Prediction Methods Research Articles

A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing

Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the model’s performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPM’s superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPM’s effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women.

A novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data.

The prediction of depression is a crucial area of research which makes it one of the top priorities in mental health research as it enables early intervention and can lead to higher success rates in treatment. Self-reported feelings by patients represent a valuable biomarker for predicting depression as they can be expressed in a lower-dimensional network form, offering an advantage in visualizing the interactive characteristics of depression-related feelings. Furthermore, the network form of data expresses high-dimensional data in a compact form, making the data easy to use as input for the machine learning processes. In this study, we applied the graph convolutional network (GCN) algorithm, an effective machine learning tool for handling network data, to predict depression-prone patients using the network form of self-reported log data as the input. We took a data augmentation step to expand the initially small dataset and fed the resulting data into the GCN algorithm, which achieved a high level of accuracy from 86-97% and an F1 (harmonic mean of precision and recall) score of 0.83-0.94 through three experimental cases. In these cases, the ratio of depressive cases varied, and high accuracy and F1 scores were observed in all three cases. This study not only demonstrates the potential for predicting depression-prone patients using self-reported logs as a biomarker in advance, but also shows promise in handling small data sets in the prediction, which is critical given the challenge of obtaining large datasets for biomarker research. The combination of self-reported logs and the GCN algorithm is a promising approach for predicting depression and warrants further investigation.

Quality index metrics with Bi-GRU-based water quality prediction

ABSTRACT The precise assessment of groundwater resources is significant in the face of growing water demands, environmental pollution, and degradation. However, traditional methods of water quality prediction are inadequate in dealing with large volumes of data or missing data. Machine learning-based prediction methods are being explored to address this issue. Currently, single-indicator techniques are commonly used, but they may not accurately forecast multimodal water quality or capture inter-indicator connections. In this study, a Bi-GRU is proposed to predicting water quality based on quality index data. To evaluate the performance of our proposed method, we utilize Kaggle Datasets, which provide a diverse range of water quality data. We compare our results with existing methods such as MLP, LSTM, and GRU, using metrics including accuracy, precision, recall, and f-score. Overall, our study demonstrates that the Bi-GRU model is highly effective in predicting water quality based on quality index data. The results of our experiments indicate that our proposed method surpasses traditional approaches like MLP, LSTM, and GRU in terms of accuracy, precision, recall, and f-score. These findings have significant implications for improving water quality monitoring and pollution prevention efforts, enabling better management of groundwater resources in the face of growing water demands and environmental challenges.

Artificial intelligence-based prediction of molecular and genetic markers for hepatitis C-related hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the main cause of mortality from cancer globally. This paper intends to classify public gene expression data of patients with Hepatitis C virus-related HCC (HCV+HCC) and chronic HCV without HCC (HCV alone) through the XGboost approach and to identify key genes that may be responsible for HCC. The current research is a retrospective case-control study. Public data from 17 patients with HCV+HCC and 35 patients with HCV-alone samples were used in this study. An XGboost model was established for the classification by 10-fold cross-validation. Accuracy (AC), balanced accuracy (BAC), sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were utilized for performance assessment. AC, BAC, sensitivity, specificity, positive predictive value, negative predictive value, and F1 scores from the XGboost model were 98.1, 97.1, 100, 94.1, 97.2, 100, and 98.6%, respectively. According to the variable importance values from the XGboost, the HAO2, TOMM20, GPC3, and PSMB4 genes can be considered potential biomarkers for HCV-related HCC. A machine learning-based prediction method discovered genes that potentially serve as biomarkers for HCV-related HCC. After clinical confirmation of the acquired genes in the following medical study, their therapeutic use can be established. Additionally, more detailed clinical works are needed to substantiate the significant conclusions in the current study.

A novel method for dry density forecasting of high-speed railway graded aggregate fillers based on the PSO-ANN model

To address the lack of intelligent prediction research on dry density, this paper proposes a machine learning-based prediction method for compaction quality of high-speed railway graded aggregate (HRGA) fillers. Firstly, to reveal the main control characteristics between HRGA fillers performance and dry density, 80 sets of vibration compaction tests were carried out. The Grey Relation Analysis (GRA) algorithm was used to quantify the degree of influence between the properties of particle gradation, particle shape and particle fragmentation, and dry density. Then, a stable database of dry density of HRGA fillers was established through 300 lab vibratory compaction tests. Finally, three Hybrid Machine Learning (ML) models (PSO-ANN, PSO-SVR, and PSO-RF) was used to fit the nonlinear relationship between the mian control characteristics and dry density. The results show that the gradation parameter b, maximum particle size dmax, and gradation parameter m are strongly correlated features, with a mean value of correlation reaching 0.77. These three features are considered the main control factors affecting the dry density and are used as input features for the subsequent ML prediction model. Moreover, the PSO-ANN model is shown to have the higher prediction accuracy with R2 = 0.9634 in the test set and lower uncertainty with U95= 0.1413, Tstat= 0.533, and SI = 0.2306 in the test set for dry density predictions. The finding can provide a significant reference for intelligent compaction.

Machine Learning Model-based Detection of Potential Genetic Markers Associated with the Diagnosis of Small-cell Lung Cancer

Background: Small-cell lung cancer (SCLC), which is in the category of intractable cancers, has a low survival rate. It is essential to understand the pathophysiological pathways underlying its development to create powerful treatment alternatives for the disease. Objectives: This study aimed to classify gene expression data from SCLC and normal lung tissue and identify the key genes responsible for SCLC. Methods: This study used microarray expression data obtained from SCLC tissue and normal lung tissue (adjacent tissue) from 18 patients. An Extreme Gradient Boosting (XGBoost) model was established for the classification by five-fold cross-validation. Accuracy (AC), balanced accuracy (BAC), sensitivity (Sens), specificity (Spec), positive predictive value (PPV), negative predictive value (NPV), and F1 scores were utilized for performance assessment. Results: AC, BAC, Sens, Spec, PPV, NPV, and F1 scores from the XGBoost model were 90%, 90%, 80%, 100%, 100%, 83.3%, and 88.9%, respectively. Based on variable importance values from the XGBoost, the HIST1H1E, C12orf56, DSTNP2, ADAMDEC1, and HMGB2 genes can be considered potential biomarkers for SCLC. Conclusion: A machine learning-based prediction method discovered genes that potentially serve as biomarkers for SCLC. After clinical confirmation of the acquired genes in the following medical study, their therapeutic use can be established in clinical practice.

Using machine learning-based binary classifiers for predicting organizational members' user satisfaction with collaboration software.

In today's digital economy, enterprises are adopting collaboration software to facilitate digital transformation. However, if employees are not satisfied with the collaboration software, it can hinder enterprises from achieving the expected benefits. Although existing literature has contributed to user satisfaction after the introduction of collaboration software, there are gaps in predicting user satisfaction before its implementation. To address this gap, this study offers a machine learning-based forecasting method. We utilized national public data provided by the national information society agency of South Korea. To enable the data to be used in a machine learning-based binary classifier, we discretized the predictor variable. We then validated the effectiveness of our prediction model by calculating feature importance scores and prediction accuracy. We identified 10 key factors that can predict user satisfaction. Furthermore, our analysis indicated that the naive Bayes (NB) classifier achieved the highest prediction accuracy rate of 0.780, followed by logistic regression (LR) at 0.767, extreme gradient boosting (XGBoost) at 0.744, support vector machine (SVM) at 0.744, K-nearest neighbor (KNN) at 0.707, and decision tree (DT) at 0.637. This research identifies essential indicators that can predict user satisfaction with collaboration software across four levels: institutional guidance, information and communication technology (ICT) environment, company culture, and demographics. Enterprises can use this information to evaluate their current collaboration status and develop strategies for introducing collaboration software. Furthermore, this study presents a novel approach to predicting user satisfaction and confirm the effectiveness of the machine learning-based prediction method proposed in this study, adding to the existing knowledge on the subject.

Prediction of Process Parameters for Ultra-Precision Optical Processing Based on Dual-Stacked LSTM

High-precision and large-aperture optical components have important applications in modern optics and optoelectronics. However, the traditional continuous polishing technology of optical components relies heavily on the processing experience of the processing personnel. The surface shape of the pitch lap is judged by frequent offline measurement of the surface shape of the processing workpiece, and then the processing personnel judges how to adjust the next process parameters through their own experience, which leads to uncertainty of processing time and low processing efficiency. In this paper, based on the historical processing data, including the surface parameters of workpieces and process parameters before and after each processing, a machine learning-based prediction method of process parameters is proposed. At first, taking the surface shape of the pitch lap as the hidden parameter of the model, a time-series mathematical model of the forward and reverse processing processes is constructed. Theoretical and experimental results show that the prediction method in this paper can effectively reduce the processing time and improve the stability of the workpiece quality.

Machine learning on protein-protein interaction prediction: models, challenges and trends.

Protein-protein interactions (PPIs) carry out the cellular processes of all living organisms. Experimental methods for PPI detection suffer from high cost and false-positive rate, hence efficient computational methods are highly desirable for facilitating PPI detection. In recent years, benefiting from the enormous amount of protein data produced by advanced high-throughput technologies, machine learning models have been well developed in the field of PPI prediction. In this paper, we present a comprehensive survey of the recently proposed machine learning-based prediction methods. The machine learning models applied in these methods and details of protein data representation are also outlined. To understand the potential improvements in PPI prediction, we discuss the trend in the development of machine learning-based methods. Finally, we highlight potential directions in PPI prediction, such as the use of computationally predicted protein structures to extend the data source for machine learning models. This review is supposed to serve as a companion for further improvements in this field.

Prediction of Phage Virion Proteins Using Machine Learning Methods

Antimicrobial resistance (AMR) is a major problem and an immediate alternative to antibiotics is the need of the hour. Research on the possible alternative products to tackle bacterial infections is ongoing worldwide. One of the most promising alternatives to antibiotics is the use of bacteriophages (phage) or phage-driven antibacterial drugs to cure bacterial infections caused by AMR bacteria. Phage-driven proteins, including holins, endolysins, and exopolysaccharides, have shown great potential in the development of antibacterial drugs. Likewise, phage virion proteins (PVPs) might also play an important role in the development of antibacterial drugs. Here, we have developed a machine learning-based prediction method to predict PVPs using phage protein sequences. We have employed well-known basic and ensemble machine learning methods with protein sequence composition features for the prediction of PVPs. We found that the gradient boosting classifier (GBC) method achieved the best accuracy of 80% on the training dataset and an accuracy of 83% on the independent dataset. The performance on the independent dataset is better than other existing methods. A user-friendly web server developed by us is freely available to all users for the prediction of PVPs from phage protein sequences. The web server might facilitate the large-scale prediction of PVPs and hypothesis-driven experimental study design.

A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control

Yaw control is one of the most promising active wake control strategies to maximize the total power generation of wind farms. Meanwhile, structural performance needs to be considered in yaw misalignment in case the adverse structural performance offsets the benefit of yaw control in power enhancement. However, an efficient and accurate prediction method for fatigue loads under yaw control is still lacking. In this study, a machine learning-based prediction method is proposed to accurately estimate the fatigue loads and power of wind turbines under yaw control. Fatigue loads at critical turbine components and corresponding power yields are selected as outputs to reflect the performance of yawed wind turbines. Since most wind turbines (WTs) are sunk into the wake flow of their upstream counterparts, the wake effects are considered with the combination of active yaw control. Besides, the full range of inflow and yaw conditions are taken into account to ensure the accuracy and practicability of the proposed model. A machine learning algorithm, support vector regression (SVR), is tuned and trained to learn the relationships between outputs and inputs. The superiority of the proposed method is verified by comparing it with another machine learning-based model in several metrics. The results show that the proposed prediction method can return high regression coefficients and low deviation, proving its accuracy and robustness. Large yaw angles and high wind speeds are found to be beneficial for further improving the prediction accuracy. The proposed fatigue loads and power prediction method is expected to make contributions to the yaw optimization and therefore benefit the wind farms.

Bayesian machine learning-based method for prediction of slope failure time

The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time (SFT). The observational and model uncertainties could lead the predicted SFT calculated from the phenomenological models to deviate from the actual SFT. Currently, very limited study has been conducted on how to evaluate the effect of such uncertainties on SFT prediction. In this paper, a comprehensive slope failure database was compiled. A Bayesian machine learning (BML)-based method was developed to learn the model and observational uncertainties involved in SFT prediction, through which the probabilistic distribution of the SFT can be obtained. This method was illustrated in detail with an example. Verification studies show that the BML-based method is superior to the traditional inverse velocity method (INVM) and the maximum likelihood method for predicting SFT. The proposed method in this study provides an effective tool for SFT prediction.

Effectiveness of automated alerting system compared to usual care for the management of sepsis

There is a large body of evidence showing that delayed initiation of sepsis bundle is associated with adverse clinical outcomes in patients with sepsis. However, it is controversial whether electronic automated alerts can help improve clinical outcomes of sepsis. Electronic databases are searched from inception to December 2021 for comparative effectiveness studies comparing automated alerts versus usual care for the management of sepsis. A total of 36 studies are eligible for analysis, including 6 randomized controlled trials and 30 non-randomized studies. There is significant heterogeneity in these studies concerning the study setting, design, and alerting methods. The Bayesian meta-analysis by using pooled effects of non-randomized studies as priors shows a beneficial effect of the alerting system (relative risk [RR]: 0.71; 95% credible interval: 0.62 to 0.81) in reducing mortality. The automated alerting system shows less beneficial effects in the intensive care unit (RR: 0.90; 95% CI: 0.73–1.11) than that in the emergency department (RR: 0.68; 95% CI: 0.51–0.90) and ward (RR: 0.71; 95% CI: 0.61–0.82). Furthermore, machine learning-based prediction methods can reduce mortality by a larger magnitude (RR: 0.56; 95% CI: 0.39–0.80) than rule-based methods (RR: 0.73; 95% CI: 0.63–0.85). The study shows a statistically significant beneficial effect of using the automated alerting system in the management of sepsis. Interestingly, machine learning monitoring systems coupled with better early interventions show promise, especially for patients outside of the intensive care unit.

Machine Learning-Based Prediction Method for Tremors Induced by Tacrolimus in the Treatment of Nephrotic Syndrome.

Tremors have been reported even with a low dose of tacrolimus in patients with nephrotic syndrome and are responsible for hampering the day-to-day work of young active patients with nephrotic syndrome. This study proposes a neural network model based on seven variables to predict the development of tremors following tacrolimus. The sensitivity and specificity of this algorithm are high. A total of 252 patients were included in this study, out of which 39 (15.5%) experienced tremors, 181 patients (including 32 patients who experienced tremors) were randomly assigned to a training dataset, and the remaining were assigned to an external validation set. We used a recursive feature elimination algorithm to train the training dataset, in turn, through 10-fold cross-validation. The classification performance of the classifer was then used as the evaluation criterion for these subsets to find the subset of optimal features. A neural network was used as a classification algorithm to accurately predict tremors using the subset of optimal features. This model was subsequently tested in the validation dataset. The subset of optimal features contained seven variables (creatinine, D-dimer, total protein, calcium ion, platelet distribution width, serum kalium, and fibrinogen), and the highest accuracy obtained was 0.8288. The neural network model based on these seven variables obtained an area under the curve (AUC) value of 0.9726, an accuracy of 0.9345, a sensitivity of 0.9712, and a specificity of 0.7586 in the training set. Meanwhile, the external validation achieved an accuracy of 0.8214, a sensitivity of 0.8378, and a specificity of 0.7000 in the validation dataset. This model was capable of predicting tremors caused by tacrolimus with an excellent degree of accuracy, which can be beneficial in the treatment of nephrotic syndrome patients.

Effects of flooding on pavement performance: a machine learning-based network-level assessment

ABSTRACT Catastrophic flood and hurricane events cause enormous adversarial impacts on roadways and their long-term performance. Despite the abundance of research attempting to understand the flood-induced damages, a critical knowledge gap in assessing roadway flood damage at a network-level exists. This study develops a holistic assessment model that evaluates network-level flood damage of roadways based on historic pavement distress data along with historic flood data. A rich volume of high-confidence historical pavement distress data was obtained from the Louisiana pavement management system. After a rigorous data pre-processing process by cross-referencing the flooded areas using the 2016 Louisiana flood map data, it was leveraged to analyze how flooding could interact with the pavement distress, thus affecting the overall performance of existing pavements. The study outcomes showed that the most flood-affected distress types include roughness and random cracking. Based on the findings from the analysis, this study developed a machine learning-based prediction method that can calculate future pavement performance after a flood event. After applying different algorithms for creating the prediction model, the eXtreme Gradient Boosting (XGB) classifier was selected because it represented the highest accuracy among other examined classifiers. Various datasets and scenarios were investigated with the developed prediction model to identify the most effective features and dataset combinations. The prediction model is expected to identify vulnerable pavement sections and facilitate network-level preventive maintenance of pavement to mitigate future flooding impacts by prioritizing resource allocations for maintenance and rehabilitation after a flood event.

A machine learning-based method for prediction of ship performance in ice: Part I. ice resistance

This article focuses on design of an Artificial Neural Network (ANN) model to estimate ship resistance in ice-covered water by using suitable ship and ice parameters. In order to develop a reliable model, as much ice resistance test data as from the ship sea trials and model test measurements are collected to train the neural network. Different features (ship design parameters and ice mechanic properties) are explored to find a suitable combination of input features. Several algorithms are tested and compared to select a good model for resistance prediction. It turns out that seven features and the Radial Basis Function - Particle Swarm Optimization algorithm (RBF-PSO) can provide a reasonable generalization model. This study shows that the ice resistance predicted by the ANN correlates well with the measured data. The model developed herein can be used as an ice resistance prediction tool with high accuracy compared to the conventional semi-empirical formulae used in polar ship design.

Development of quantitative model of a local lymph node assay for evaluating skin sensitization potency applying machine learning CatBoost

The estimated concentrations for a stimulation index of 3 (EC3) in murine local lymph node assay (LLNA) is an important quantitative value for determining the strength of skin sensitization to chemicals, including cosmetic ingredients. However, animal testing bans on cosmetics in Europe necessitate the development of alternative testing methods to LLNA. A machine learning-based prediction method can predict complex toxicity risks from multiple variables. Therefore, we developed an LLNA EC3 regression model using CatBoost, a new gradient boosting decision tree, based on the reliable Cosmetics Europe database which included data for 119 substances. We found that a model using in chemico/in vitro tests, physical properties, and chemical information associated with key events of skin sensitization adverse outcome pathway as variables showed the best performance with a coefficient of determination (R2) of 0.75. In addition, this model can indicate the variable importance as the interpretation of the model, and the most important variable was associated with the human cell line activation test that evaluate dendritic cell activation. The good performance and interpretability of our LLNA EC3 predictable regression model suggests that it could serve as a useful approach for quantitative assessment of skin sensitization.

A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection.

The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's urgent to develop computational approaches to detect drug-drug interactions. In this paper, we conduct a comprehensive review of state-of-the-art computational methods falling into three categories: literature-based extraction methods, machine learning-based prediction methods and pharmacovigilance-based data mining methods. Literature-based extraction methods detect DDIs from published literature using natural language processing techniques; machine learning-based prediction methods build prediction models based on the known DDIs in databases and predict novel ones; pharmacovigilance-based data mining methods usually apply statistical techniques on various electronic data to detect drug-drug interaction signals. We first present the taxonomy of drug-drug interaction detection methods and provide the outlines of three categories of methods. Afterwards, we respectively introduce research backgrounds and data sources of three categories, and illustrate their representative approaches as well as evaluation metrics. Finally, we discuss the current challenges of existing methods and highlight potential opportunities for future directions.

Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory

This paper investigates the possibility of using machine learning technology to correct wave height series numerical predictions. This is done by incorporating numerical predictions into long short-term memory (LSTM). Specifically, a novel ocean wave height series prediction framework, referred to as numerical long short-term memory (N-LSTM), is introduced. The N-LSTM takes a combined wave height representation, which is formed of a current wave height measurement and a subsequent Simulating Waves Nearshore (SWAN) numerical prediction, as the input and generates the corrected numerical prediction as the output. The correction is achieved by two modules in cascade, i.e., the LSTM module and the Gaussian approximation module. The LSTM module characterizes the correlation between measurement and numerical prediction. The Gaussian approximation module models the conditional probabilistic distribution of the wave height given the learned LSTM. The corrected numerical prediction is obtained by sampling the conditional probabilistic distribution and the corrected numerical prediction series is obtained by iterating the N-LSTM. Experimental results validate that our N-LSTM effectively lifts the accuracy of wave height numerical prediction from SWAN for the Bohai Sea and Xiaomaidao. Furthermore, compared with the state-of-the-art machine learning based prediction methods (e.g., residual learning), the N-LSTM achieves better prediction accuracy by 10% to 20% for the prediction time varying from 3 to 72 h.

Predicting Increase in Demand for Public Buses in University Students Daily Life Needs: Case Study Based on a City in Japan

Accessibility and economic sustainability of public bus services (PBS) have been in a continuous decline in Japan’s countryside. Rural cities also suffer from population transformation toward industrial centers experiencing rapid economic growth. In the present study, we reviewed the current demand status of PBS in Kitami, a rural city in Japan that hosts a national university. The investigation was performed by examining students’ daily lives using a survey to collect data representing a portion of the population. The objective was to predict the change in demand rate for PBS concerning the necessities of everyday life from the perspective of university students as potential users of PBS. Intuitively, decision-makers at every level display a distinct prejudice toward alternatives that intend to change the long-lasting status quo, hence in the question sequence, a two-step verification probe was used to reveal a person’s actual perceived opinion. Accordingly, the respondents’ initial demand rate for PBS was around 60%; however, this score increased to 71% in the secondary confirmation. Afterward, using machine learning-based prediction methods, we could predict this demand at over 90% of F-measure, with the most reliable and stable prediction method reaching 80% by other daily life indicators’ weight. Finally, we supplied thorough evidence for our approach’s usability by collecting and processing the data’s right set regarding this study’s objective. This method’s highlighted outcomes would help to reduce the local governments’ and relevant initiatives’ adaptability time to demands and improve decision-making flexibility.