Multiple Heart Diseases Prediction using Logistic Regression with Ensemble and Hyper Parameter tuning Techniques

Ambesange, Sateesh; Vijayalaxmi, A; Sridevi, S.; Venkateswaran,; Yashoda, B S

doi:10.1109/worlds450073.2020.9210404

Cited by 28 publications

(14 citation statements)

References 7 publications

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“…To build the ML model, this research uses several classifiers, namely Logistic regression (LR) [24], Support vector machine (SVM) [25], and Random Forest (RF) [26].…”

Section: Methodology and Working Principlementioning

confidence: 99%

IoT and Blockchain-Based Mask Surveillance System for COVID-19 Prevention Using Deep Learning

Rahman¹,

Mudawi²,

Alazeb³

et al. 2022

Computers, Materials &Amp; Continua

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On the edge of the worldwide public health crisis, the COVID-19 disease has become a serious headache for its destructive nature on humanity worldwide. Wearing a facial mask can be an effective possible solution to mitigate the spreading of the virus and reduce the death rate. Thus, wearing a face mask in public places such as shopping malls, hotels, restaurants, homes, and offices needs to be enforced. This research work comes up with a solution of mask surveillance system utilizing the mechanism of modern computations like Deep Learning (DL), Internet of things (IoT), and Blockchain. The absence or displacement of the mask will be identified with a raspberry pi, a camera module, and the operations of DL and Machine Learning (ML). The detected information will be sent to the cloud server with the mechanism of IoT for real-time data monitoring. The proposed model also includes a Blockchain-based architecture to secure the transactions of mask detection and create efficient data security, monitoring, and storage from intruders. This research further includes an IoT-based mask detection scheme with signal bulbs, alarms, and notifications in the smartphone. To find the efficacy of the proposed method, a set of experiments has been enumerated and interpreted. This research work finds the highest accuracy of 99.95% in the detection and classification of facial masks. Some related experiments with IoT and Block-chain-based integration have also been performed and calculated the corresponding experimental data accordingly. A System Usability Scale (SUS) has been accomplished to check the satisfaction level of use and found the SUS score of 77%. Further, a comparison among existing solutions on three emergent technologies is included to track the significance of the proposed scheme. However, the proposed system can be an efficient mask surveillance system for COVID-19 and workable in real-time mask detection and classification.

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“…To build the ML model, this research uses several classifiers, namely Logistic regression (LR) [24], Support vector machine (SVM) [25], and Random Forest (RF) [26].…”

Section: Methodology and Working Principlementioning

confidence: 99%

IoT and Blockchain-Based Mask Surveillance System for COVID-19 Prevention Using Deep Learning

Rahman¹,

Mudawi²,

Alazeb³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…In this approach, data scaling has been performed as [44] shows that the high deviation between the numeric values of the various characteristics can force ML algorithms to bias toward large values. However, hyperparameter optimization can also improve the performance in this type of case, as shown in [45][46][47]. As seen in 18 evaluates all performance measures using this combination.…”

Section: Approach Bmentioning

confidence: 99%

A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE-ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset

Nishat

Faisal

Ratul

et al. 2022

Scientific Programming

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Heart failure is a chronic cardiac condition characterized by reduced supply of blood to the body due to impaired contractile properties of the muscles of the heart. Like any other cardiac disorder, heart failure is a serious ailment limiting the activities and curtailing the lifespan of the patient, most often resulting in death sooner or later. Detection of survival of patients with heart failure is the path to effective intervention and good prognosis in terms of both treatment and quality of life of the patient. Machine learning techniques can be critical in this regard since they can be used to predict the survival of patients with heart failure in advance, allowing patients to receive appropriate treatment. Hence, six supervised machine learning algorithms have been studied and applied to analyze a dataset of 299 individuals from the UCI Machine Learning Repository and predict their survivability from heart failure. Three distinct approaches have been followed using Decision Tree Classifier, Logistic Regression, Gaussian Naïve Bayes, Random Forest Classifier, K-Nearest Neighbors, and Support Vector Machine algorithms. Data scaling has been performed as a preprocessing step utilizing the standard and min–max scaling method. However, grid search cross-validation and random search cross-validation techniques have been employed to optimize the hyperparameters. Additionally, the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) data resampling technique are utilized, and the performances of all the approaches have been compared extensively. The experimental results clearly indicate that Random Forest Classifier (RFC) surpasses all other approaches with a test accuracy of 90% when used in combination with SMOTE-ENN and standard scaling technique. Therefore, this comprehensive investigation portrays a vivid visualization of the applicability and compatibility of different machine learning algorithms in such an imbalanced dataset and presents the role of the SMOTE-ENN algorithm and hyperparameter optimization for enhancing the performances of the machine learning algorithms.

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“…One of the most critical threats to human beings in the current times is heart-related diseases. The paper (Ambesange et al, 2020) uses normalization and outlier detection was done to improve the data, obtained from the UCI repository. Application of several feature selection methods, like the Extra tree's classifier, Random search, and other techniques are made for tuning.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Application of several feature selection methods, like the Extra tree's classifier, Random search, and other techniques are made for tuning. Seven models are developed with various features (Ambesange et al, 2020). One of the models for which the dimension reduction technique namely Kernel Principal Component Analysis (PCA) was employed on the dataset and then the Grid Search method was used to give 100% accuracy, which suggests the model is over-fitted, thus requiring much more processing.…”

Section: Literature Reviewmentioning

confidence: 99%

Cardiovascular Disease Prediction using Various Machine Learning Algorithms

Swain¹,

Parmar²,

Shah³

et al. 2022

Journal of Computer Science

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Almost one-third of all deaths caused around the world were caused due to cardiovascular diseases. Even if death was not the result, much cost is incurred during the treatment of such diseases. But much of these deaths and treatments could have been prevented with prior action. Advance knowledge of the symptoms and consequently proper care can lead us to avoid such diseases. Thus, current research proposes a highly effective model to predict the presence of heart diseases. Bad eating habits, smoking, stress, and genetics are some of the factors that influence our body mechanisms, which actually cause various irregularities in our hearts and thus adversely affect our bodies. The body mechanisms influenced by external factors have been included to prepare an efficient model to predict the probability of cardiovascular diseases. UCI repository dataset has been utilized for the training and testing purpose in our model. Then accordingly, five different algorithms namely Logistic Regression, Support Vector Machine, Multi-Layer Perceptron (MLP) Classifier with Principal Component Analysis (PCA), Deep Neural Network, Bootstrap Aggregation using Random Forests are executed on our filtered dataset to find which one is the optimum out of all of them. Pre-processing techniques have been extensively used to filter out the dataset. The data processing along with the different models employed make this a sound paper, which could be utilized for real-world cases without any prior modification. Different places around the world would take different factors into account, hence our model can be used as it takes all critical factors from several datasets.

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Multiple Heart Diseases Prediction using Logistic Regression with Ensemble and Hyper Parameter tuning Techniques

Cited by 28 publications

References 7 publications

IoT and Blockchain-Based Mask Surveillance System for COVID-19 Prevention Using Deep Learning

IoT and Blockchain-Based Mask Surveillance System for COVID-19 Prevention Using Deep Learning

A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE-ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset

Cardiovascular Disease Prediction using Various Machine Learning Algorithms

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