Machine Learning Algorithms For Prediction Research Articles

Wind Speed Prediction in Jerusalem Using Machine Learning Algorithms: A Case Study of Using ANFIS and KNNR

Wind energy is acknowledged for its status as a renewable energy source that offers several advantages, including its low cost of electricity generation, abundant availability, high efficiency, and minimal environmental impact. The prediction of wind speed using machine learning algorithms is crucial for various applications, such as wind energy planning and urban development. This paper presents a case study on wind speed prediction in Palestine Jerusalem city using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and K-Nearest Neighbors Regression (KNNR) algorithms. The study evaluates their performance using multiple metrics, including root mean square (RMSE), bias, and coefficient of determination R<sup>2</sup>. ANFIS demonstrates good accuracy with lower RMSE (0.196) and minimal bias (0.0003). However, there is room for improvement in capturing overall variability (R<sup>2</sup> = 0.15). In contrast, KNNR exhibits a higher R<sup>2</sup> (0.4093), indicating a better fit, but with a higher RMSE (1.4209). These results demonstrated the potential of machine learning algorithms in wind speed prediction, which can lead to optimize the wind energy generation at specific site, and reducing the cost of energy production. This study provides insights into the applicability of ANFIS and KNNR in wind speed prediction for Jerusalem and suggests future research directions. The outcomes have practical implications for wind energy planning, urban development, and environmental assessments in similar regions.

Artificial intelligence and machine learning for early cancer prediction and response

Worldwide, cancer claims more lives than any other disease. Although cancer detection, prognosis, and treatment have all advanced, one major obstacle is the lack of personalized, data-driven care. Artificial intelligence (AI), used to forecast and automate numerous malignancies, has emerged as a viable solution for increasing healthcare accuracy and patient outcomes. Artificial intelligence applications in cancer include risk evaluation, early detection, patient prognosis prediction, and treatment selection based on comprehensive data. Machine learning (ML), a form of artificial intelligence that allows computers to learn from training data, is very successful in predicting breast, brain, lung, liver, and prostate cancers. Indeed, AI and machine learning have outperformed physicians in predicting cancer. These technologies can potentially enhance the diagnosis, prediction, and quality of life of patients with a wide range of disorders, not just cancer. As a result, it is critical to enhance existing AI and ML technologies and create new programs to help patients. This article discusses the use of AI and machine learning algorithms in cancer prediction, including its present uses, future possibilities, and limitations.

Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies

ObjectiveSuicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts.MethodA systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach.ResultsForty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing.The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction.Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts.ConclusionsThe effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.

DESIGN A CIRCUIT FOR HUMIDITY PREDICTION USING CMOS TECHNOLOGY AND IMPLEMENTING ANN

This research paper presents the knowledge about Humidity prediction model based on Arduino Uno, DHT11 Sensor and 16*2 LCD display. The model combines real-time data acquisition with predictive machine learning algorithm to estimate future humidity levels. The microcontroller of Arduino processes from the DHT sensor and shows the predicted and actual humidity data in the LCD display. This system gives a low-cost and user-friendly solution for humidity monitoring in various environments. Keyword: Arduino UNO, DHT-11, 16*2 LCD

Performance Evaluation of Hybrid Machine Learning Algorithms for Online Lending Credit Risk Prediction

ABSTRACT Peer-to-Peer systems are still in the early stages of development when it comes to the processing of credit and the appraisal of the risk associated with it. In this study, we used a hybrid convolutional neural network with logistic regression, a gradient-boosting decision tree, and a k-nearest neighbor to predict the credit risk in a P2P lending club. The lending clubs publicly available P2P loan data was used to train the model. In order to address the issue of data imbalance within the dataset, specifically between the non-defaulter and defaulter classes, the synthetic minority oversampling technique sampling approach is utilized. We developed the architecture of our hybrid model by removing the fully connected layer with the soft-max, which is the final layer of the fully connected CNN model and replaced by LR, GBDT, and k-NN algorithms. The experimental results show that the hybrid CNN-kNN model outperforms the CNN-GBDT and CNN-LR models based on the performance metrics accuracy, recall, F1-score, and area under the curve for both all input and important features. This shows that hybrid machine learning models effectively identify and categorize credit risk in peer-to-peer lending clubs, hence assisting in financial loss prevention.

Analyzing the Energy Consumption of Smart Home and Price Prediction

This paper describes a system that is used to gather data from Smart Home sensors measuring energy consumption, analyze and predict future consumption by using machine learning algorithm for prediction. It focuses on the architecture of the system as well as the need for such system to exist in practice – why the consumption should be measured at all and how that measurement can be put to good use for achieving energy efficiency.

Analysis of Machine Learning Algorithms for prediction and classification of Breast Cancer

Analysis of Machine Learning Algorithms for prediction and classification of Breast Cancer

Machine Learning Modeling to Predict Atrial Fibrillation Detection in Embolic Stroke of Undetermined Source Patients.

In patients with embolic stroke of undetermined source (ESUS), occult atrial fibrillation (AF) has been implicated as a key source of cardioembolism. However, only a minority acquire implantable cardiac loop recorders (ILRs) to detect occult paroxysmal AF, partly due to financial cost and procedural inconvenience. Without the initiation of appropriate anticoagulation, these patients are at risk of increased ischemic stroke recurrence. Hence, cost-effective and accurate methods of predicting AF in ESUS patients are highly sought after. We aimed to incorporate clinical and echocardiography data into machine learning (ML) algorithms for AF prediction on ILRs in ESUS. This was a single-center cohort study that included 157 consecutive patients diagnosed with ESUS from October 2014 to October 2017 who had ILR evaluation. We developed four ML models, with hyperparameters tuned, to predict AF detection on an ILR. The median age of the cohort was 67 (IQR 59-74) years old and the median monitoring duration was 1051 (IQR 478-1287) days. Of the 157 patients, 32 (20.4%) had occult AF detected on the ILR. Support vector machine predicted for AF with a 95% confidence interval area under the receiver operating characteristic curve (AUC) of 0.736-0.737, multilayer perceptron with an AUC of 0.697-0.708, XGBoost with an AUC of 0.697-0.697, and random forest with an AUC of 0.663-0.674. ML feature importance found that age, HDL-C, and admitting heart rate were important non-echocardiography variables, while peak mitral A-wave velocity and left atrial volume were important echocardiography parameters aiding this prediction. Machine learning modeling incorporating clinical and echocardiographic variables predicted AF in ESUS patients with moderate accuracy.

Usage of Weka Software Based On Machine Learning Algorithms for Prediction of Liver Fibrosis/Cirrhosis

The liver, a life-sustaining organ, plays a substantial role in many body functions. Liver diseases have become an important world health problem in terms of prevalence, incidences, and mortalities. Liver fibrosis/cirrhosis is great of importance, because if not treated in time liver cancer could be occurred and spread to other parts of the body. For this reason, early diagnosis of liver fibrosis/cirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosis/cirrhosis based on demographic and blood values. In this context, random forest, k nearest neighbour, C4.5 decision tree, K-star, random tree and reduced error pruning tree algorithms were used. Two distinct approaches were employed to evaluate the performances of machine learning algorithms. In the first approach, the entire features of dataset were utilized, while in the second approach, only the features selected through principal component analysis were used. Each approach was rigorously assessed using both 10-fold cross-validation and data splitting (70% train and 30% test) techniques. By conducting separate evaluations for each approach, a comprehensive understanding of the effectiveness of utilizing all features versus extracted features based principal component analysis was attained, providing valuable insights into the impact of feature dimensionality reduction on model performance. In this study, all analyses were implemented on WEKA data mining tool. In the first approach, the classification accuracies of random forest algorithm were 89.72% and 90.75% with the application of data splitting (70%-30%) and cross-validation techniques, respectively. In the second approach, where feature reduction is performed using principal component analysis technique, the accuracy values obtained from data splitting and cross-validation techniques of random forest algorithm were 88.61% and 88.83%, respectively. The obtained results revealed out that random forest algorithm outperformed for both approaches. Besides, the application of principal component analysis technique negatively affected the classification performance of used machine learning algorithms. It is thought that the proposed model will guide specialist physicians in making appropriate treatment decisions for patients with liver fibrosis/cirrhosis, potentially leading to death in its advanced stages.

Comparative Analysis of Machine Learning Algorithms for Liver Disease Prediction: SVM, Logistic Regression, and Decision Tree

This study compares Support Vector Machine (SVM), Logistic Regression, and Decision Tree algorithms for liver disease prediction using a dataset sourced from Kaggle, comprising 20,000 training records and approximately 1,000 test records. The research evaluates the algorithms based on performance metrics, including accuracy, precision, recall, and F1-score. SVM emerged as the most effective model with an accuracy of 85%, followed by Logistic Regression with 82% and Decision Tree with 79%. The findings underscore the significance of algorithm selection in healthcare applications and highlight SVM's potential for early detection and intervention in liver disease cases, paving the way for improved patient outcomes and healthcare management. Future work will focus on refining the algorithms and validating the results with larger and more diverse datasets to enhance predictive accuracy and robustness further.

Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens

The identification and characterization of immunogenic tumor antigens are essential for cancer vaccine development. In light of the impracticality of isolating and evaluating each putative antigen individually, in silico prediction algorithms, particularly those utilizing machine learning (ML) approaches, play a pivotal role. These algorithms significantly reduce the experimental workload necessary for discovering vaccine candidates. In this study, we employed six supervised ML methods on a dataset comprising 212 experimentally validated human tumor peptide antigens and an equal number of non-antigenic human peptides to develop models for immunogenicity prediction. These methods encompassed k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). The models underwent validation through internal cross-validation within 10 groups from the training set and were further assessed using an external test set. Remarkably, the kNN model demonstrated superior performance, recognizing 90% of the known immunogens in the test set. The RF model excelled in the identification of non-immunogens, accurately classifying 93% of them in the test set. The three top-performing ML models according to multiple evaluation metrics (SVM, RF, and XGBoost) are to be subsequently integrated into the new version of the VaxiJen server, facilitating tumor antigen prediction through a majority voting mechanism.

Development and Impact Analysis of a Multi-Pandemic Real-Time Data Dashboard: A Comparative Analytical Approach

This research presents the development and implementation of a Multi Pandemic Real-Time Data Dashboard, designed to facilitate enhanced comparative analysis across various global pandemics. Utilizing a combination of real-time data acquisition, statistical modeling, and predictive analytics, the dashboard provides a dynamic platform for analyzing pandemic trends and outcomes. Our methodology involved integrating diverse data sources, employing mathematical models such as the SIR (Susceptible, Infected, Recovered) model and machine learning algorithms for data processing and trend prediction. Key findings indicate that the dashboard effectively identifies patterns in pandemic spread and impact, offering valuable insights for public health decision-making. The research underscores the significance of advanced data analytics in managing public health crises, highlighting the dashboard’s potential in aiding proactive pandemic response and policy formulation.

Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles

The combined impact of freeze-thaw cycles and dynamic loads significantly influences the long-term durability of rock engineering in high-cold regions. Consequently, investigating the dynamic compressive strength (DCS) of rocks subjected to freeze-thaw cycles has emerged as a crucial area of scientific research to advance rock engineering construction in cold regions. Presently, the determination of the DCS of rocks under freeze-thaw cycles primarily relies on indoor experiments. However, this approach has faced criticism due to its drawbacks, including prolonged duration, high costs, and reliance on rock samples. To address these limitations, the exploration of using artificial intelligence technology to develop more accurate and convenient DCS prediction models for rocks under freeze-thaw cycles is a promising attempt. In this context, this paper introduces a DCS prediction model for rocks under freeze-thaw cycles, which integrates the Sparrow Search Algorithm (SSA) with Random Forest (RF). Firstly, employing a dataset of 216 samples, Principal Component Analysis (PCA) is utilized to reduce the dimensionality of ten influential factors. Subsequently, five optimization algorithms are employed to optimize the hyperparameters of both the BP and RF algorithms. Finally, a comprehensive evaluation and comparative analysis are carried out to assess the predictive performance of the optimized model, using evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).The research findings demonstrate that the SSA-RF model exhibits the best predictive performance, surpassing the other nine models in terms of generalization. The prediction model proposed in this study has good applicability for predicting DCS of freeze-thaw rock in cold regions, and also provides new ideas for the combination of machine learning and rock mass engineering in cold regions.

Accuracy of Artificial Intelligence in Predicting Facial Changes Post-Orthognathic Surgery: A Comprehensive Scoping Review.

Accurate prediction of facial soft tissue changes post-orthognathic surgery is crucial for treatment planning and patient communication. Current models pose limitations due to the complexity of facial biomechanics and individual variances. Artificial intelligence (AI) has emerged as an important tool in many disciplines, including the dental field. The aim of this scoping review is to assess the accuracy of AI in predicting facial changes post-orthognathic surgery in comparison to traditional models. Explore the strengths and limitations of the current AI models. Following PRISMA-DTA guidelines, a comprehensive search was conducted manually and through Medline, Embase, Web of Science, Scopus, and Google Scholar databases was conducted, focusing on studies that applied AI models with various machine learning and deep learning algorithms for post-surgical outcome prediction. Selection criteria were based on the PICO format, emphasizing studies that compared AI-predicted outcomes with actual post-surgical results. Literature was searched until January 31, 2024. The initial search result yielded 1579 records. After screening and assessment for eligibility, seven studies met the inclusion criteria, with publication dates ranging from 2009 to 2023. Several AI algorithms were evaluated on different orthognathic surgical procedures, revealing the high predictive accuracy of AI models across various facial regions. AI demonstrates significant potential for enhancing the precision of facial outcome predictions following orthognathic surgery. However, despite the promising results, limitations such as small sample sizes and a lack of external validation were noted. Further research with larger, more diverse datasets and standardized validation methods is essential for optimizing AI's clinical utility. Key words:Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Orthognathic Surgery, Facial Soft-tissue Prediction, Predictive Accuracy, Orthodontics.

Predicting the performance of a photovoltaic unit via machine learning methods in the existence of finned thermal storage unit

The process of cooling the photovoltaic (PV) cells is essential since increasing solar cell temperatures reduces electrical effectiveness. One suggested method to reduce cell temperature is employing PCM (phase change material) mixed with nanomaterial. Thermoelectric modules between the layers are also recommended to increase electrical efficiency. By taking advantage of machine learning predictive algorithms, the objective is to forecast the effectiveness of a PV unit equipped with finned thermal storage unit in existence of nanomaterials, while maintaining an appropriate accuracy based on two evaluation metrics throughout prediction. According to the data analytics of the simulation, the selected models used to achieve this goal are linear regression, polynomial regression, lasso regression, and an Auto-Regressive Integrated Moving Average (ARIMA) model. These models were then examined employing the root mean squared error (RMSE) and mean absolute percentage error (MAPE) across three cases, namely plain mode, mode with fins, and plain mode with the effect of gravity. The test RMSE and MAPE of these models across the above-mentioned cases were then calculated for comparison purposes, where the best-performing model for predicting both PCM temperature and melting fraction (MF) was ARIMA, with an average RMSE of 0.11, and an average MAPE of 0.0003 for predicting PCM temperature and an average RMSE of 0.003, and an average MAPE of 0.0034 for predicting MF across all cases. The ARIMA model was then used to predict the MF, PCM temperature, and PV temperature of the last case, namely mode with fins and the effect of gravity. Finally, this model was also used to predict the time at which the PCMs are completely melted in all modes, as well as to predict PV temperature. The total electrical efficiency of the mode with fins and the effect of gravity were calculated using the predicted PV temperature, yielding a result of 13.993 %.

Multiple Disease Prediction using ML

Abstract: This study proposes a multi-disease prediction system integrating assessments for diabetes, heart disease, and Parkinson’s disease. The system leverages machine learning algorithms like Logistic Regression, Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN) to predict disease risk through a unified user interface. We delve into the system design process, emphasizing the importance of defining system compo- nents and exploring the utility of modeling languages. The research explores the potential of SVMs, including linear and non-linear variations, for disease prediction. We analyze existing literature on applying machine learning algorithms for disease prediction and discuss their potential for disease classification. Finally, the abstract addresses challenges and future directions in disease prediction, aiming to provide valuable insights for further research and development efforts.

Comparative Analysis of Supervised Machine Learning Algorithms for COVID-19 Prediction

With the emergence of COVID-19 as an unprecedented pandemic, the health structure of both the developed and underdeveloped world not only seemed stranded but terrible. The human interface was faced with the dilemma of infection causing the health workers fall prey to the disease while identifying the presence of the disease among the patients. Given the nature of the disease, it is needed to mitigate the effects of spread by resorting to technological advancements for diagnosis of the disorder using machine learning algorithms. In this paper, three supervised machine learning algorithms; Decision Tree, Naïve Bayes, and Logistic Regression have been utilized for the prediction of the disease encompassing nine attributes considering various combinations of symptoms. A comparative analysis of the algorithms used revealed that Decision Trees with 99% accuracy and 98% precision, rendered it the most viable and accurate technique for the diagnosis of COVID-19 disease.

Multiple Disease Prediction System Using ML

Abstract: The increasing prevalence of diverse diseases presents a challenge to global healthcare systems, underscoring the need for innovative and efficient methods for early detection and preventive measures. This paper explores the application of machine learning algorithms in multiple disease prediction to enhance diagnostic accuracy and enable timely intervention. Leveraging diverse health-related data sources, including medical records and genomic information, comprehensive predictive models are developed. A multi-faceted machine learning approach integrates support vector machines, decision trees, neural networks, and ensemble learning methods to analyze complex data patterns. Feature selection and dimensionality reduction techniques optimize model performance and interpretability. The development of a predictive system involves essential steps such as data collection, preprocessing, and model training, followed by evaluation using metrics like accuracy and recall. Integration of Flask for web application development facilitates user interaction and prediction functionality. Deployment, testing, debugging, and ongoing maintenance ensure system efficiency and compliance with regulatory requirements for healthcare data security and privacy.

Hybrid Machine Learning Algorithms for Prediction of Failure Modes and Punching Resistance in Slab-Column Connections with Shear Reinforcement

This study presents a data-driven model for identifying failure modes (FMs) and predicting the corresponding punching shear resistance of slab-column connections with shear reinforcement. An experimental database that contains 328 test results is used to determine nine input variables based on the punching shear mechanism. A comparison is conducted between three typical machine learning (ML) approaches: random forest (RF), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost) and two hybrid optimized algorithms: grey wolf optimization (GWO) and whale optimization algorithm (WOA). It was found that the XGBoost classifier had the highest accuracy rate, precision, and recall values for FM identification. In testing, WOA-XGBoost has the best accuracy in predicting punching shear resistance, with R2, MAE, and RMSE values of 0.9642, 0.087 MN, and 0.126 MN, respectively. However, a comparison between experimental values and calculated values derived from classical analytical methods clearly demonstrates that existing design codes need to be improved. Additionally, Shapley additive explanations (SHAP) were applied to explain the model’s predictions, with factors categorized according to their impact on failure modes and punching shear resistance. By modifying these parameters, punching resistance can be improved while reducing unpredictable failure. With the proposed hybrid algorithms, it is possible to determine the failure modes and the punching shear resistance of slabs during the preliminary stages of the construction.

Prompt Design through ChatGPT’s Zero-Shot Learning Prompts: A Case of Cost-Sensitive Learning on a Water Potability Dataset

Datasets used in AI applications for human health require careful selection. In healthcare, machine learning (ML) models are fine-tuned to reduce errors, and our study focuses on minimizing errors by generating code snippets for cost-sensitive learning using water potability datasets. Water potability ensures safe drinking water through various scientific methods, with our approach using ML algorithms for prediction. We preprocess data with ChatGPT-generated code snippets and aim to demonstrate how zero-shot learning prompts in ChatGPT can produce reliable code snippets that cater to cost-sensitive learning. Our dataset is sourced from Kaggle. We compare model performance metrics of logistic regressors and gradient boosting classifiers without additional code fine-tuning to check the accuracy. Other classifier performance metrics are compared with results of the top 5 code authors on the Kaggle scoreboard. Cost-sensitive learning is crucial in domains like healthcare to prevent misclassifications with serious consequences, such as type II errors in water potability assessment.