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Effective Prediction of Mortality by Heart Disease Among Women in Jordan Using the Chi-Squared Automatic Interaction
Salam Bani Hani1, Muayyad Ahmad1
1Clinical Nursing Department, School of Nursing, The University of Jordan, Amman, Jordan.
Insights
This study used machine learning to predict heart disease mortality in women. The Chi-squared Automatic Interaction Detection model accurately identified key predictors of cardiovascular death in women.
Area of Science:
- Cardiovascular disease research
- Artificial intelligence in healthcare
- Data mining in medicine
Background:
- Heart disease risk in women is a significant public health concern.
- Machine learning algorithms (MLA) are increasingly used to analyze large datasets for health predictions.
- Identifying specific mortality predictors in women is crucial for targeted interventions.
Purpose of the Study:
- To predict heart disease mortality among women using an artificial intelligence-based machine learning algorithm.
- To identify key variables associated with cardiovascular mortality in a female population.
Main Methods:
- Retrospective analysis of electronic health records from 2028 Jordanian women diagnosed with heart disease (2015-2021).
- Data preprocessing included cleaning, organizing, and eliminating redundant information.
- Evaluation of nine artificial intelligence models to determine the most accurate for mortality prediction.
Main Results:
- The Chi-squared Automatic Interaction Detection (CHAID) model achieved the highest accuracy (93.25%) and AUC (0.825).
- Common diagnoses included angina pectoris (62.3%) and congestive heart failure (37.7%).
- Key predictors of mortality were age, high systolic blood pressure (>187 mm Hg), congestive heart failure diagnosis, pulse pressure (>98 mm Hg), and low oxygen saturation (<93%).
Conclusions:
- Machine learning, specifically the CHAID model, effectively predicts cardiovascular mortality in women using electronic health record data.
- The study identified critical clinical predictors for mortality, offering a practical tool for risk assessment and clinical decision-making.
- Big data analytics can enhance the understanding and management of heart disease in women.
Background:
Many current studies have claimed that the actual risk of heart disease among women is equal to that in men. Using a large machine learning algorithm (MLA) data set to predict mortality in women, data mining techniques have been used to identify significant aspects of variables that help in identifying the primary causes of mortality within this target category of the population.
Objective:
This study aims to predict mortality caused by heart disease among women, using an artificial intelligence technique-based MLA.
Methods:
A retrospective design was used to retrieve big data from the electronic health records of 2028 women with heart disease. Data were collected for Jordanian women who were admitted to public health hospitals from 2015 to the end of 2021. We checked the extracted data for noise, consistency issues, and missing values. After categorizing, organizing, and cleaning the extracted data, the redundant data were eliminated.
Results:
Out of 9 artificial intelligence models, the Chi-squared Automatic Interaction Detection model had the highest accuracy (93.25%) and area under the curve (0.825) among the build models. The participants were 62.6 (SD 15.4) years old on average. Angina pectoris was the most frequent diagnosis in the women's extracted files (n=1,264,000, 62.3%), followed by congestive heart failure (n=764,000, 37.7%). Age, systolic blood pressure readings with a cutoff value of >187 mm Hg, medical diagnosis (women diagnosed with congestive heart failure were at a higher risk of death [n=31, 16.58%]), pulse pressure with a cutoff value of 98 mm Hg, and oxygen saturation (measured using pulse oximetry) with a cutoff value of 93% were the main predictors for death among women.
Conclusions:
To predict the outcomes in this study, we used big data that were extracted from the clinical variables from the electronic health records. The Chi-squared Automatic Interaction Detection model-an MLA-confirmed the precise identification of the key predictors of cardiovascular mortality among women and can be used as a practical tool for clinical prediction.
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