Predicting Coronary Artery Disease Using Machine Learning DOI Open Access
Keshab R. Dahal, Nawa Raj Pokhrel,

Pramesh Subedi

и другие.

International Journal of Statistics and Probability, Год журнала: 2024, Номер 13(2), С. 1 - 1

Опубликована: Май 29, 2024

Developing a predictive model for detecting Coronary Artery Disease (CAD) is crucial due to its high global fatality rate of approximately 17.9 million people annually. With the advancements in artificial intelligence, availability large-scale data, and increased access computational capability, it feasible create robust models that can detect CAD with precision. This study aims build assist health workers timely detection ultimately reduce mortality. performs comparative analysis four supervised classification machine learning algorithms- Logistic regression (LR), Support vector (SVM), Extreme gradient boosting (XGBoost), Artificial neural network (ANN),  predicting case-control status patient. Chi-squared lasso criteria are employed select most relevant ones from available features. The performance compared using sensitivity, specificity, accuracy, area under receiver operating characteristic (ROC) curve (AUC). experimental results indicate LR effective accurate among tested, implementation improve clinical settings.

Язык: Английский

Evaluating LSTM and NARX neural networks for wind speed forecasting and energy optimization in Tetouan, Northern Morocco DOI Creative Commons
Wissal Masmoudi,

Abdelouahed Djebli,

F. El Moussaoui

и другие.

Energy Exploration & Exploitation, Год журнала: 2025, Номер unknown

Опубликована: Янв. 7, 2025

Generating electricity from renewable sources is crucial for advancing toward a low-carbon economy, with wind power playing significant role. Effective energy management essential meeting societal needs and protecting the environment. This study aims to optimize production by improving accuracy of speed predictions. Building on previous research comparing MLP, NARX, Elman models Tetouan City, we introduce novel comparison between nonlinear autoregressive exogenous inputs (NARX) model long short-term memory (LSTM) network. Utilizing MATLAB, analyzed 12 years meteorological data City determine which provides most accurate Our results reveal that LSTM significantly outperforms NARX model, achieving lower values mean absolute error (MAE = 0.18855), squared (MSE 0.0666), root (RMSE 0.25808). demonstrates network's superior capability handle complex, long-term data. These findings offer valuable insights enhancing in similar regions, highlighting model's potential optimization efficiency.

Язык: Английский

Процитировано

0
Real Estate Market Prediction Using Deep Learning Models DOI
Ramchandra Rimal, Binod Rimal,

Hum Nath Bhandari

и другие.

Annals of Data Science, Год журнала: 2024, Номер unknown

Опубликована: Июнь 4, 2024

Язык: Английский

Процитировано

1
Predicting the Direction of NEPSE Index Movement with News Headlines Using Machine Learning DOI Creative Commons
Keshab R. Dahal,

Ankrit Gupta,

Nawa Raj Pokhrel

и другие.

Econometrics, Год журнала: 2024, Номер 12(2), С. 16 - 16

Опубликована: Июнь 11, 2024

Predicting stock market movement direction is a challenging task due to its fuzzy, chaotic, volatile, nonlinear, and complex nature. However, with advancements in artificial intelligence, abundant data availability, improved computational capabilities, creating robust models capable of accurately predicting now feasible. This study aims construct predictive model using news headlines predict direction. It conducts comparative analysis five supervised classification machine learning algorithms—logistic regression (LR), support vector (SVM), random forest (RF), extreme gradient boosting (XGBoost), neural network (ANN)—to the next day’s close price Nepal Stock Exchange (NEPSE) index. Sentiment scores from are computed Valence Aware Dictionary for Reasoning (VADER) TextBlob sentiment analyzer. The models’ performance evaluated based on sensitivity, specificity, accuracy, area under receiver operating characteristic (ROC) curve (AUC). Experimental results reveal that all perform equally well when Similarly, exhibit almost identical VADER analyzer, except minor variations AUC SVM vs. LR ANN. Moreover, relatively better analyzer compared These findings further validated through statistical tests.

Язык: Английский

Процитировано

1
Predicting Coronary Artery Disease Using Machine Learning DOI Open Access
Keshab R. Dahal, Nawa Raj Pokhrel,

Pramesh Subedi

и другие.

International Journal of Statistics and Probability, Год журнала: 2024, Номер 13(2), С. 1 - 1

Опубликована: Май 29, 2024

Developing a predictive model for detecting Coronary Artery Disease (CAD) is crucial due to its high global fatality rate of approximately 17.9 million people annually. With the advancements in artificial intelligence, availability large-scale data, and increased access computational capability, it feasible create robust models that can detect CAD with precision. This study aims build assist health workers timely detection ultimately reduce mortality. performs comparative analysis four supervised classification machine learning algorithms- Logistic regression (LR), Support vector (SVM), Extreme gradient boosting (XGBoost), Artificial neural network (ANN),  predicting case-control status patient. Chi-squared lasso criteria are employed select most relevant ones from available features. The performance compared using sensitivity, specificity, accuracy, area under receiver operating characteristic (ROC) curve (AUC). experimental results indicate LR effective accurate among tested, implementation improve clinical settings.

Язык: Английский

Процитировано

0