Effective Heart disease prediction framework using Random Forest and Logistic regression

Naveen, Soumyalatha; Ravindran, Sourabh; P, Shreya G; Ameen, Sadeeq

doi:10.1109/vitecon58111.2023.10157078

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Due to the risk of overfitting in a tree, it is always beneficial to combine multiple trees to obtain more convincing results. The randomness of the RF algorithm mainly consists of two parts: the features and the instances that are randomly selected for each tree, both of which are obtained through the bootstrap technique according to the pre-determined ratio [15].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

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Heart disease prediction utilizing machine learning techniques

Chen

2024

TMBLS

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Heart disease is an international public health issue and a significant health risk for many people. The World Health Organization (WHO) reported that heart disease has been identified as one of the primary causes of death. Owing to its rapid development and application in many areas, Machine Learning has become an effective technique for predicting heart disease. Machine learning, along with large-scale medical data and advanced algorithms, can assist healthcare professionals in accurately predicting the risk of heart disease, thus providing early intervention and treatment for patients. This research paper uses the “heart_2020_cleaned.csv” dataset, containing 319795 instances and 18 attributes, of which 70% of instances were randomly selected for the training set and 30% for testing. Applying machine learning algorithms in data mining such as Decision Tree (DT), LightGBM, Random Forest (RF) and Logistic Regression (LR) to forecast heart disease. Before constructing models, data cleaning, feature selection and hyperparameter tuning processes were done, aiming to explore the potential patterns among data. Comparative Analysis was conducted on the external test set to compare the prediction performance of different models at the same level. The result reported that the highest accuracy achieved with LightGBM was 76.9%, followed by Logistic Regression and Random Forest, with Decision Tree being the worst.

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Section: Random Forest (Rf)mentioning

confidence: 99%

“…LR algorithm is a popular model in machine learning due to its computational efficiency and interpretability [15].…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Heart disease prediction utilizing machine learning techniques

Chen

2024

TMBLS

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“…Analyzing and quantifying the relationship between a dependent variable and a set of independent factors is called regression analysisS. Naveen et al (2023). Parametric coefficients on the independent variables allow for predictions of the dependent variable's future values, and hence an equation expressing this relationship is often used.…”

Section: Logistic Regressionmentioning

confidence: 99%

“…Furthermore, highdimensional data often offers computational hurdles and the possibility of overfitting [23]. To overcome these concerns, we use Principal Component Analysis (PCA), a dimensionality reduction approach that converts the original feature space into a lower-dimensional subspace while retaining crucial properties [24][25]. As a consequence, the feature representation for categorization is more efficient and controllable.…”

Section: Introductionmentioning

confidence: 99%

Youtube Comment Feature Selection And Classification Using Fused Machine Learning

R. Rooba,

2023

tjjpt

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The exponential rise of internet platforms, notably YouTube, has resulted in a massive volume of user-generated material, including video comments. Understanding audience input and enhancing user experience require analyzing and forecasting the mood of YouTube comments. This work provides a comprehensive method for YouTube comment prediction and sentiment classification that combines feature selection using Recursive Feature Elimination (RFE), Elastic Net Random Forest with Logistic Regression (RF with LR), and Principal Component Analysis (PCA). The first stage is to choose the most informative features from a given dataset using RFE, a common approach for doing so. RFE aids in the elimination of unnecessary or redundant features, resulting in enhanced model performance and decreased computing complexity. The Elastic Net Random Forest with Logistic Regression (RF with LR) technique is then used to construct a robust sentiment classification model. Elastic Net regularization combines the advantages of both L1 (Lasso) and L2 (Ridge) regularization, allowing for improved feature selection and management of multicollinearity concerns. By integrating many decision trees, the Random Forest ensemble approach improves the model's predictive potential even more. We employ Principal Component Analysis (PCA) to increase the classification model's effectiveness and solve possible difficulties created by high-dimensional data. PCA decreases the dataset's complexity while retaining its fundamental qualities, resulting in a more manageable and efficient feature space for classification. Finally, we compare the performance of three prominent classifiers on the preprocessed dataset: Linear Support Vector Machine (LSVM), Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Decision Tree (DT). We can select the best-performing model for YouTube comment categorization by comparing these classifiers.

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Materials-Driven Soft Wearable Bioelectronics for Connected Healthcare

Gong,

Lu,

Yin

et al. 2024

Chem. Rev.

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In the era of Internet-of-things, many things can stay connected; however, biological systems, including those necessary for human health, remain unable to stay connected to the global Internet due to the lack of soft conformal biosensors. The fundamental challenge lies in the fact that electronics and biology are distinct and incompatible, as they are based on different materials via different functioning principles. In particular, the human body is soft and curvilinear, yet electronics are typically rigid and planar. Recent advances in materials and materials design have generated tremendous opportunities to design soft wearable bioelectronics, which may bridge the gap, enabling the ultimate dream of connected healthcare for anyone, anytime, and anywhere. We begin with a review of the historical development of healthcare, indicating the significant trend of connected healthcare. This is followed by the focal point of discussion about new materials and materials design, particularly low-dimensional nanomaterials. We summarize material types and their attributes for designing soft bioelectronic sensors; we also cover their synthesis and fabrication methods, including top-down, bottom-up, and their combined approaches. Next, we discuss the wearable energy challenges and progress made to date. In addition to front-end wearable devices, we also describe back-end machine learning algorithms, artificial intelligence, telecommunication, and software. Afterward, we describe the integration of soft wearable bioelectronic systems which have been applied in various testbeds in real-world settings, including laboratories that are preclinical and clinical environments. Finally, we narrate the remaining challenges and opportunities in conjunction with our perspectives.

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Effective Heart disease prediction framework using Random Forest and Logistic regression

Cited by 5 publications

References 8 publications

Heart disease prediction utilizing machine learning techniques

Heart disease prediction utilizing machine learning techniques

Youtube Comment Feature Selection And Classification Using Fused Machine Learning

Materials-Driven Soft Wearable Bioelectronics for Connected Healthcare

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