METHOD: This paper is motivated by the gap in the literature, thus proposes an algorithm that measures the strength of the significant features that contribute to heart disease prediction. The study is aimed at predicting heart disease based on the scores of significant features using Weighted Associative Rule Mining.

RESULTS: A set of important feature scores and rules were identified in diagnosing heart disease and cardiologists were consulted to confirm the validity of these rules. The experiments performed on the UCI open dataset, widely used for heart disease research yielded the highest confidence score of 98% in predicting heart disease.

METHODS: We recruited and analyzed SARS-CoV-2-infected adult patients (age ≥18 years) who were admitted to the ICU at Jaber Al-Ahmad Al Sabah Hospital, Kuwait, between March 1, 2020, and April 30, 2020. The risk factors associated with in-hospital mortality were assessed using multiple regression analysis.

RESULTS: We recruited a total of 103 ICU patients in this retrospective cohort. The median age of the patients was 53 years and the fatality rate was 45.6%; majority (85.5%) were males and 37% patients had more than 2 comorbidities. Preexisting hypertension, moderate/severe acute respiratory distress syndrome, lymphocyte count <0.5 × 109, serum albumin <22 g/L, procalcitonin >0.2 ng/mL, D-dimer >1,200 ng/mL, and the need for continuous renal replacement therapy were significantly associated with mortality.