Nabil Hewahi

Elham Musaaed, Nabil Hewahi, Abdulla Alasaadi

In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area for these algorithms such as modeling of diseases. The majority of these applications employ supervised rather than unsupervised ML algorithms. In addition, each year, the amount of data in medical science grows rapidly. Moreover, these data include clinical and Patient-Related Factors (PRF), such as height, weight, age, other physical characteristics, blood sugar, lipids, insulin, etc., all of which will change continually over time. Analysis of historical data can help identify disease risk factors and their interactions, which is useful for disease diagnosis and prediction. This wealth of valuable information in these data will help doctors diagnose accurately and people can become more aware of the risk factors and key indicators to act proactively. The purpose of this study is to use six supervised ML approaches to fill this gap by conducting a comprehensive experiment to investigate the correlation between PRF and Diabetes, Stroke, Heart Disease (HD), and Kidney Disease (KD). Moreover, it will investigate the link between Diabetes, Stroke, and KD and PRF with HD. Further, the research aims to compare and evaluate various ML algorithms for classifying diseases based on the PRF. Additionally, it aims to compare and evaluate ML algorithms for classifying HD based on PRF as well as Diabetes, Stroke, Asthma, Skin Cancer, and KD as attributes. Lastly, HD predictions will be provided through a Web-based application on the most accurate classifier, which allows the users to input their values and predict the output.

Yasmeen Aldossary, Nabil Hewahi, Abdulla Alasaadi

With industrial and technological development and the increasing demand for electric power, wind energy has gradually become the fastest-growing and most environmentally friendly new energy source. Nevertheless, wind power generation is always accompanied by uncertainty due to the wind speed's volatility. Wind speed forecasting (WSF) is essential for power grids' dispatch, stability, and controllability, and its accuracy is crucial to effectively using wind resources. Therefore, this study proposes a novel WSF framework for stationary data based on a hybrid decomposition method and the Bidirectional Long Short-term Memory (BiLSTM) to achieve high forecasting accuracy for the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia. The hybrid decomposition method combines the Wavelet Packet Decomposition (WPD) and the Seasonal Adjustment Method (SAM). The SAM method eliminates the seasonal component of the decomposed subseries generated by WPD to reduce forecasting complexity. The BiLSTM is applied to forecast all the deseasonalized decomposed subseries. Five years of hourly wind speed observations acquired from a location in the Al-Jouf region were used to prove the effectiveness of the proposed model. The comparative experimental results, including 27 other models, demonstrated the proposed model's superiority in single and multiple WSF with an overall average mean absolute error of 0.176549, root mean square error of 0.247069, and R-squared error of 0.985987.