Across the globe, heart diseases rank as the top cause of death, with their incidence steadily rising. However, early detection before a cardiac event (e.g., cardiac arrest) remains a significant challenge. Although the healthcare sector possesses extensive data on heart disease, the effective use of this data for timely detection is essential to protect from such events. This paper proposes an innovative approach using fuzzy logic (FL), convolutional neural network (CNN) models, and feature selection to more accurately assess the risk of heart attacks. Our study also emphasizes the importance of data preprocessing, including data transformation, cleaning, and normalization, to facilitate th... More >

The challenge of accurately estimating effort for software development projects is critical for project managers (PM) and researchers. A common issue they encounter is missing data values in datasets, which complicates effort estimation (EE). While several models have been introduced to address this issue, none have proven entirely effective. The Analogy-Based Effort Estimation (ABEE) model is the most widely used approach, relying on historical data for estimation. However, the common practice of deleting cases or cells with missing observations results in a reduction of statistical power and negatively impacts the performance of ABEE, leading to inefficiencies and biases. This study employ... More >