Machine learning-based identification of patients with a cardiovascular defect

Louridi, Nabaouia; Douzi, Samira; Ouahidi, Bouabid El

doi:10.1186/s40537-021-00524-9

Cited by 38 publications

(14 citation statements)

References 28 publications

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“…Notably, studies have found that utilizing a subset of features yields better performance compared to using all available features [10]. The ROC curve visually illustrates the trade-off to between the true positive rate and the false positive rate for a binary classification model across different classification thresholds, aiding in model evaluation and comparison [11]. The test outcomes reveal a True Positive Rate (TP Rate) of 0.943 in the PCR Area, along with an ROC Area of 0.976 Class.…”

Section: IIImentioning

confidence: 99%

Heart Failure Prediction Using Machine Learning

Maddala,

Modugulla,

Pulusu

et al. 2024

IJRASET

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Cardiovascular Disease (CVD) currently stands as the leading cause of death worldwide. Clinical data analytics encounter a significant challenge in accurately predicting cardiac disease. The healthcare industry generates vast volumes of raw data, necessitating its transformation into meaningful insights through machine learning techniques. The objective is to leverage machine learning models to improve the predictability of survival among cardiac patients. This study employs machine learning classifiers: Random Forest, Gradient Boosting classifier, Extra Tree Classifier, XG-Boost, Ada Boost and Hybrid models. The Synthetic Minority Oversampling Technique (SMOTE) addresses the challenge posed by imbalanced datasets. Experimental results indicate that employing the SMOTE technique enhances the accuracy of the chosen classifier's predictions. Among these classifiers, Hybrid Model stands out with the highest accuracy of 89.82% when applied to predicting the survival of cardiac illness afterimplementing SMOTE

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Section: IIImentioning

confidence: 99%

Heart Failure Prediction Using Machine Learning

Maddala,

Modugulla,

Pulusu

et al. 2024

IJRASET

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“…There have already been several research on the topic of predicting nutritional status and finding their factors using the machine learning (ML) approach. Multiple illnesses have been predicted using ML systems such as anemia [ 28 , 29 ], acute appendicitis [ 30 ], cardiovascular defect [ 31 ], covid 19 [ 32 , 33 ], diabetes [ 27 , 34 – 37 ], hypertension [ 38 ], low birth weight [ 39 – 42 ] utilizing a variety of demographic and health survey datasets as well as common risk factors of the diseases. Several studies had been conducted previously based on malnutrition using machine learning approaches.…”

Section: Introductionmentioning

confidence: 99%

Assessing risk factors for malnutrition among women in Bangladesh and forecasting malnutrition using machine learning approaches

Turjo,

Rahman

2024

BMC Nutr

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Background This paper presents an in-depth examination of malnutrition in women in Bangladesh. Malnutrition in women is a major public health issue related to different diseases and has negative repercussions for children, such as premature birth, decreased infection resistance, and an increased risk of death. Moreover, malnutrition is a severe problem in Bangladesh. Data from the Bangladesh Demographic Health Survey (BDHS) conducted in 2017-18 was used to identify risk factors for malnourished women and to create a machine learning-based strategy to detect their nutritional status. Methods A total of 17022 women participants are taken to conduct the research. All the participants are from different regions and different ages. A chi-square test with a five percent significance level is used to identify possible risk variables for malnutrition in women and six machine learning-based classifiers (Naïve Bayes, two types of Decision Tree, Logistic Regression, Random Forest, and Gradient Boosting Machine) were used to predict the malnutrition of women. The models are being evaluated using different parameters like accuracy, sensitivity, specificity, positive predictive value, negative predictive value, $$F_1$$ F 1 score, and area under the curve (AUC). Results Descriptive data showed that 45% of the population studied were malnourished women, and the chi-square test illustrated that all fourteen variables are significantly associated with malnutrition in women and among them, age and wealth index had the most influence on their nutritional status, while water source had the least impact. Random Forest had an accuracy of 60% and 60.2% for training and test data sets, respectively. CART and Gradient Boosting Machine also had close accuracy like Random Forest but based on other performance metrics such as kappa and $$F_1$$ F 1 scores Random Forest got the highest rank among others. Also, it had the highest accuracy and $$F_1$$ F 1 scores in k-fold validation along with the highest AUC (0.604). Conclusion The Random Forest (RF) approach is a reasonably superior machine learning-based algorithm for forecasting women’s nutritional status in Bangladesh in comparison to other ML algorithms investigated in this work. The suggested approach will aid in forecasting which women are at high susceptibility to malnutrition, hence decreasing the strain on the healthcare system.

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“…Some healthcare applications and services are digitally accessible via a mobile device. Self-monitoring has the potential to improve health daily [15]. The purpose of this evaluation is to establish the present demand and market offer, as well as to identify chances for excellent health.…”

Section: Introductionmentioning

confidence: 99%

Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach

Naeem,

Senapati,

Bhuva

et al. 2024

IEEE Access

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This research proposes a successful technique for identifying individuals using feature extraction methods and signal processing approaches. The novelty of this study is in the use of feature extraction methods in conjunction with other signal-processing approaches. While working with the sensors, an artificial neural network (ANN) technique is employed to identify the scent patterns that are present in persons. The numerous gases released by the human body are measured using ten different kinds of sensors, all of which are metal oxide semiconductors. Before using ANN patterns to generate patterns from sensor data, it is important to scan and extract sensory information from that data. Each participant is recognized and scanned for a totally of 1000 different characteristics during the course of the multiple investigations, which are conducted across a variety of time periods that include 5, 10, 15, and 20 people. Because of the varying time periods, signals from sensors are received in analog form, which is then transformed by Arduino into digital form. It is necessary to train an architecture on the data set that has been created. The benchmarks that are employed for the assessment of the model that is presented for the identification of human odor include sensitivity, f-measures, accuracy, and specificity, among other things. Experiments are carried out using the assessment measures, and the findings demonstrate that this model has an accuracy of greater than 85 % in most cases.

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Machine learning-based identification of patients with a cardiovascular defect

Cited by 38 publications

References 28 publications

Heart Failure Prediction Using Machine Learning

Heart Failure Prediction Using Machine Learning

Assessing risk factors for malnutrition among women in Bangladesh and forecasting malnutrition using machine learning approaches

Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach

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