Logistic regression technique for prediction of cardiovascular disease

Ambrish, G; Ganesh, Bharathi; Ganesh, Anitha; Srinivas, Chetana; Dhanraj,; Mensinkal, Kiran

doi:10.1016/j.gltp.2022.04.008

Cited by 45 publications

(14 citation statements)

References 4 publications

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“…As per the research study proposed by G. Ambrish et al, the authors used to train and test combination ratios of 90:10, 80:20, 70:30, 60:40 and 50:50. Their proposed model [14] obtained the accuracy of 87.10% for the 90:10 train and test split. Another study was developed by Rohit Bharti et al [15] using an Artificial neural network for the prediction of disease.…”

Section: Related Workmentioning

confidence: 99%

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GA-SLE: A hybrid algorithm for heart disease prediction using feature selection mechanism

Kushwaha

Thirunavukkarasan

2022

Preprint

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Heart disease is the leading cause of death around the globe, killing more people than cancer. A study published in the Journal of the American Heart Association shows that gene variants in our genes can increase our risk of heart failure by as much as 40%. To make matters worse, the number of people with advanced heart failure is expected to rise by 30% by the year 2030. Some of the most common heart disease symptoms include chest pain, shortness of breath, and fatigue. It has been observed that machine learning can provide efficient heart disease prediction over a large amount of data thereby, empowering the patients and health care experts with the knowledge to make more accurate decisions at an appropriate time-bound. The central aspect of our proposed research is to build up a hybrid machine learning classifier using a Genetic Algorithm with a Super learner ensemble (GA-SLE) for the detection of heart disease with increased accuracy. In our study, the proposed system is compared with various machine learning algorithms such as Random Forest (RF), Multilayer Perceptron (MLP), K- Nearest Neighbour (KNN), Extra Tree (ET), Extreme Gradient Boosting (XGB), Super Vector Classifier (SVC), Stochastic Gradient Descent(SGD), AdaBoost, Decision Tree(CART), Gradient Boosting(GBM) using 10-Fold cross-validation and hyperparameter tuning. A regressed comparative analysis of all three approaches has been performed with the help of a table and plot. The proposed GA-SLE outperforms all other ML classifiers, obtaining a prediction accuracy of 99.8% with minimum error loss. As per our findings, the current GA-SLE classifier enables the practitioner to detect and diagnose the sickness in the early stage of the disease.

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Section: Related Workmentioning

confidence: 99%

“…The prediction was applied with a combination of data split ratios such as 90:10, 80:20, 70:30, 60:40, 50:50. [14] ANN 94.2 ANN classification was used over a dataset containing 14 attributes. [15] Vooting 83 Prediction analysis with the help of vooting ensemble was performed.…”

Section: Referencesmentioning

confidence: 99%

GA-SLE: A hybrid algorithm for heart disease prediction using feature selection mechanism

Kushwaha

Thirunavukkarasan

2022

Preprint

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“…Several types of supervised ML algorithms were utilized in this study for the accurate diagnosis prediction of BC, including weak and strong classifiers. LR is a robust decision-making tool which mainly utilized for classification-related tasks, it works like a statistical algorithm that analyzes the interrelation between a group of independent and dependent binary variables [20]. k-NN is a non-parametric ML algorithm used for classification that does not provide any decision based on the underlying data [21].…”

Section: Classifiersmentioning

confidence: 99%

Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization

Hridoy,

Arni,

Ghosh

et al. 2024

IJECE

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Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.

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“…The model, given a collection of predictor variables, determines the likelihood that the binary outcome is 1, and then, using a user-specified threshold, assigns the observation to one of two categories. Assuming a linear relationship between the predictor variables and the log-odds of the binary outcome, probability estimation is helpful for situations where there is a chance of an event occurring [20].…”

Section: Logistic Regressionmentioning

confidence: 99%

Cardiovascular Disease (CVD) Prediction Using Machine Learning Techniques With XGBoost Feature Importance Analysis

2023

IJFMR

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Cardiovascular diseases (CVD) are a type of illnesses in the cardiovascular system including coronary, rheumatic heart and cerebrovascular. The leading causes of disease burden and mortality worldwide are CVDs. CVD can cause a wide range of consequences, which can lower standard of life and sometimes cause death. This emphasizes the requirement for the establishment of a technique that can ensure an exact and prompt prediction of the risk of CVD in patients. This study investigates effective CVD prediction system using several Machine Learning (ML) classification models. Rigorous data analysis through several preprocessing techniques as well as feature importance analysis has been performed through Spearman Correlation Analysis and XGboost feature importance technique. Finally, classification has been accomplished through Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) using a standard benchmark dataset collected from IEEEDataPort. Highest accuracy of 95% has been achieved through Random Forest (RF). The findings of this study will assist professionals in the medical field in the early diagnosis of cardiovascular disease in patients.

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Logistic regression technique for prediction of cardiovascular disease

Cited by 45 publications

References 4 publications

GA-SLE: A hybrid algorithm for heart disease prediction using feature selection mechanism

GA-SLE: A hybrid algorithm for heart disease prediction using feature selection mechanism

Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization

Cardiovascular Disease (CVD) Prediction Using Machine Learning Techniques With XGBoost Feature Importance Analysis

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