Machine Learning Algorithms For Prediction Research Articles

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

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

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.

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.

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.

Application of Artificial Intelligence in Tissue Engineering.

Tissue engineering, a crucial approach in medical research and clinical applications, aims to regenerate damaged organs. By combining stem cells, biochemical factors, and biomaterials, it encounters challenges in designing complex 3D structures. Artificial intelligence (AI) enhances tissue engineering through computational modeling, biomaterial design, cell culture optimization, and personalized medicine. This review explores AI applications in organ tissue engineering (bone, heart, nerve, skin, cartilage), employing various machine learning (ML) algorithms for data analysis, prediction, and optimization. Each section discusses common ML algorithms and specific applications, emphasizing the potential and challenges in advancing regenerative therapies.

Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia

In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of anemia among youth girls in Ethiopia. A total of 5642 weighted samples of young girls from the 2016 Ethiopian Demographic and Health Survey dataset were utilized. The data underwent preprocessing, with 80% of the observations used for training the model and 20% for testing. Eight machine learning algorithms were employed to build and compare models. The model performance was assessed using evaluation metrics in Python software. Various data balancing techniques were applied, and the Boruta algorithm was used to select the most relevant features. Besides, association rule mining was conducted using the Apriori algorithm in R software. The random forest classifier with an AUC value of 82% outperformed in predicting anemia among all the tested classifiers. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having more than 5 family size were the top attributes to predict anemia. Association rule mining was identified the top seven best rules that most frequently associated with anemia. The random forest classifier is the best for predicting anemia. Therefore, making it potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt anemia among youth girls.

Ensemble Machine Learning Algorithm for Diabetes Prediction in Maiduguri, Borno State

Ensemble Machine Learning Algorithm for Diabetes Prediction in Maiduguri, Borno State

Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits.

The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person's brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual's brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy.

An Analysis of Modern Machine Learning Algorithms for Rainfall Prediction

Agriculture has a significant importance in the Indian economy. Rainfall is crucial for agriculture, however these days, predicting rainfall has become a very difficult subject. A good rainfall forecast enables people to plan ahead, take safety measures, and have better crop-related strategies. Both nature and humanity are being severely impacted by global warming, which also hastens the shift in climatic conditions. Due to the warming of the air and rising ocean level, floods are occurring and farmed fields are becoming drier. Unseasonable and excessive amounts of rainfall are a result of unfavourable climate change. One of the finest methods for learning about the rain and climate is to anticipate rain. The primary goal of this study is to accurately describe the climate to the clients from a variety of aspects, including agriculture, research, power generation, etc. to understand the requirement for changing the environment and the variables such as temperature, humidity, precipitation, and wind speed that ultimately lead to rainfall forecast. Predicting rain is difficult since it relies on geographic places as well. Machine learning, an expanding branch of artificial intelligence, aids in rainfall forecasting. For the purpose of forecasting the rainfall in this research study, we will use a dataset from the UCI repository that has many properties. The major goal of this work is to analyse a system for predicting rainfall and to do it more accurately by using machine learning classification algorithms.

Comparative Analysis of use of Machine Learning Algorithm for Prediction of Sales

Predicting sales holds immense importance for businesses, particularly for large retailers. It significantly influences decision-making processes and their subsequent execution. For big retail stores engaged in marketing and sales, accurate sales prediction is crucial for effectively allocating resources, estimating sales revenue, and devising future strategies. It empowers these stores to grasp market trends, make informed decisions, and adapt to evolving conditions, ensuring long-term viability and prosperity. Given the dynamic nature of trends and market conditions, traditional forecasting methods face significant challenges in keeping pace with rapid changes. This is where the application of machine learning algorithms becomes indispensable, especially when dealing with large and complex datasets. This paper delves into the exploration of various machine learning algorithms and models to predict sales, conducting comparative analyses to assess their performance.The overarching aim is to identify the most suitable algorithm or combination of algorithms through comprehensive analysis. Furthermore, the paper scrutinizes how the characteristics of the dataset impact the performance of these algorithms, offering valuable insights for selecting the most appropriate machine learning algorithm based on performance analysis. By leveraging these insights, businesses can enhance their sales prediction capabilities and make informed decisions, thereby maximizing their success in the marketplace.

Machine Learning based Early-detection of Lung Cancer Using CT Scan for the early Identification and Classification

The current research is aimed at studying the application of machine learning algorithms for the early prediction and detection of lung cancer taking the images of CT scans and MR scans. In this respect, the dataset from kaggle includes 3200 images from diverse sources, which are pre-processed and applied for the process of feature extraction, using traditional methods and deep learning systems, which include Convolutional Neural Networks, VGG-16, VGG-19, and RNN, while after the training the systems are evaluated in accordance with precision, recall, f1 score, and accuracy. As a result, it is evidenced that the best model is VGG-19 with the highest accuracy of 97.86%, while follows VGG-19 the VGG-16 the CNN model, and the RNN, implying effective implications for clinical practice. Certainly, the results of the research help to create a non-invasive, effective, and fast tool, using by clinicians in their practice. The use of machine learning algorithms for the process of early prediction and detection would be helpful for the timely treatment of patients and personalized treatment plans. In such a way, based on the current research, it could be summarized that the best models are constantly developed and gradually implemented in practice. However, more significant collaboration between the researchers, clinicians, and the industry is needed to have the full implementation of applied methods in practice, having fast results and timely actions taken by clinicians.

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 and C4.5 decision tree algorithms were used and these algorithms were implemented on WEKA data mining tool. The obtained results revealed out that random forest algorithm outperformed in term of all evaluation metrics (90.91% accuracy, 90% specificity, 90% precision, 91.8% recall, 0.909 F-measure and 0.962 ROC) as compared with other algorithms.

Symptom-Based Disease Prediction: A Machine Learning Approach

The advent of machine learning techniques has revolutionized various sectors, including healthcare. This project concentrates on leveraging machine learning algorithms for disease prediction based on symptoms. With a dataset comprising 132 symptoms and 41 diseases, the aim is to develop a robust predictive model capable of accurately diagnosing diseases given a set of symptoms. The process involves several key steps. Initially, the dataset is preprocessed to handle missing values, encode categorical variables, and normalize the data. To determine which symptoms are most pertinent to the prognosis of a disease, feature selection techniques are utilized. Various machine learning algorithms, including decision trees, support vector machines, random forests, and XGBoost, were explored to determine the most effective prediction model. XGBoost, in particular, emerges as one of the topperforming models because of its capacity to manage complicated relationships within the data and its effectiveness in handling imbalanced datasets. To evaluate the models' performance, evaluation criteria like accuracy, precision, recall, and F1-score are used. Moreover, to enhance model performance and avoid overfitting, techniques like crossvalidation and hyperparameter tuning are utilized. The proposed system holds significant potential in aiding healthcare professionals in diagnosing diseases promptly and accurately, thereby improving patient outcomes and reducing healthcare costs. It is important to note that the model needs further validation on diverse datasets and regular updates to remain relevant in clinical settings.

Comparative Analysis of Machine Learning Algorithms for Daily Cryptocurrency Price Prediction

Comparative Analysis of Machine Learning Algorithms for Daily Cryptocurrency Price Prediction

Analysis of stock forecasting based on LSTM

The use of the long short-term memory (LSTM) machine learning algorithm for stock prediction is widely used contemporarily. This study the feasibility of price prediction based on LSTM. This study first collected extensive data on stock prices, including the closing prices, volumes, and other relevant indicators, for several companies. Then, it implemented an LSTM-based model using this data, which was trained on a dataset containing historical stock price information over a fixed period. The models performance was evaluated using a separate test set. Based on the analysis, LSTM is successful in forecasting stock a remarkable degree of precision, rendering it an invaluable asset for investors aiming to make well-informed investment choices. Its ability to capture long-term dependencies and patterns within time series data makes it an ideal choice for forecasting future trends in financial markets. By leveraging this model, investors can gain valuable insights into potential market movements and adjust their investment strategies accordingly. Overall, the promising results obtained from applying LSTM-based models highlight their potential value in supporting investors' decision-making processes and ultimately improving investment outcomes.

Comparative analysis of machine learning algorithms for Diabetes Mellitus prediction

Type II Diabetes Mellitus (T2DM) has become an increasingly prevalent disease due to the rising number of the obese population. Even though this disorder is the leading cause of more severe health complications, studies have shown that T2DM is largely preventable if detected in an early phase. To improve current diagnosis and prognosis procedures, this study aims to examine three machine learning algorithmsK-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Classification (SVC)and their potential in making accurate predictions on the outcomes of the Pima Indians Diabetes Dataset (PIDD). After training and 5-fold cross validation, the results show that the RF algorithm has the highest accuracy at 75.25%, followed by SVC at 74.91% and KNN at 71.01%. In addition, feature importances were evaluated for all three models, yet we observed a drastic difference in the top-ranked features across different models, which implies that more training and larger datasets are necessary before realizing these computational approaches into practice. Nevertheless, the potential of these approaches highlighted in this study demonstrated that machine learning is a burgeoning strategy in clinical use and in solving real-world problems.

Comparative analysis of Deep Learning and Machine Learning algorithms for emoji prediction from Arabic text

Comparative analysis of Deep Learning and Machine Learning algorithms for emoji prediction from Arabic text