Performance of different Machine Learning Techniques for the Prediction of Heart Diseases

Breast cancer (BC) disease is the most common and rapidly spreading disease across the globe. This disease can be prevented if identified early, and this eventually reduces the death rate. Machine learning (ML) is the most frequently utilized technology in research. Cancer patients can benefit from early detection and diagnosis. Using machine learning approaches, this research proposes an improved way of detecting breast cancer. To deal with the problem of imbalanced data in the class and noise, the Synthetic Minority Oversampling Technique (SMOTE) has been used. There are two steps in the suggested task. In the first phase, SMOTE is utilized to decrease the influence of imbalance data issues, and subsequently, in the next phase, data is classified using the Naive Bayes classifier, decision trees classifier, Random Forest, and their ensembles. According to the experimental analysis, the XGBoost-Random Forest ensemble classifier outperforms with 98.20% accuracy in the early detection of breast cancer.

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“…The authors [19] describe A-SMOTE, an advanced strategy to deal with the data imbalance problem. The steps are described below.…”

Section: Methodsmentioning

confidence: 99%

Performance Analysis of XGBoost Ensemble Methods for Survivability with the Classification of Breast Cancer

et al. 2022

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“…RF works by using the training data to create several decision trees. In the case of classification, every tree suggests output as a class; also the class with greatest number of outputs is selected as the final outcome [ 29 ]. In order to build, number of trees must be specified.…”

Section: Exploratory Knowledgementioning

confidence: 99%

AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease

Mahesh

Kumar

et al. 2022

Computational Intelligence and Neuroscience

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As a result of technology improvements, various features have been collected for heart disease diagnosis. Large data sets have several drawbacks, including limited storage capacity and long access and processing times. For medical therapy, early diagnosis of heart problems is crucial. Disease of heart is a devastating human disease that is quickly increasing in developed and also developing countries, resulting in death. In this type of disease, the heart normally fails to provide enough blood to different body parts in order to allow them to perform their regular functions. Early, as well as, proper diagnosis of this condition is very critical for averting further damage and also to save patients’ lives. In this work, machine learning (ML) is utilized to find out whether a person has cardiac disease or not. Both the types of ensemble classifiers, namely, homogeneous as well as heterogeneous classifiers (formed by combining two separate classifiers), have been implemented in this work. The data mining preprocessing using Synthetic Minority Oversampling Technique (SMOTE) has been employed to cope with the imbalance problem of the class as well as noise. The proposed work has two steps. SMOTE is used in the initial phase to reduce the impact of data imbalance and the second phase is classifying data using Naive Bayes (NB), decision tree (DT) algorithms, and their ensembles. The experimental results demonstrate that the AdaBoost-Random Forest classifier provides 95.47% accuracy in the early detection of heart disease.

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“…This classifier made use of training data to model many tree structures, and each tree suggested a class as an output, with vast numbers of results selected as the overall outcome [21]. The building structure requires specific numbers of trees and, as such can be used as a means of bagging and aggregating bootstrap data.…”

Section: Random Forest (Rf) Classifiermentioning

confidence: 99%

Predictive Analysis of Mental Health Conditions Using AdaBoost Algorithm

Ogunseye

Adenusi²,

Nwanakwaugwu

et al. 2022

paradigmplus

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The presented research responds to increased mental illness conditions worldwide and the need for efficient mental health care (MHC) through machine learning (ML) implementations. The datasets employed in this investigation belong to a Kaggle repository named "Mental Health Tech Survey." The surveys for the years 2014 and 2016 were downloaded and aggregated. The prediction results for bagging, stacking, LR, KNN, tree class, NN, RF, and Adaboost yielded 75.93%, 75.93%, 79.89%, 90.42%, 80.69%, 89.95%, 81.22%, and 81.75% respectively. The AdaBoost ML model performed data cleaning and prediction on the datasets, reaching an accuracy of 81.75%, which is good enough for decision-making. The results were further used with other ML models such as Random Forest (RF), K-Nearest Neighbor (KNN), bagging, and a few others, with reported accuracy ranging from 81.22 to 75.93 which is good enough for decision making. Out of all the models used for predicting mental health treatment outcomes, AdaBoost has the highest accuracy.

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Performance of different Machine Learning Techniques for the Prediction of Heart Diseases

Cited by 21 publications

References 7 publications

Performance Analysis of XGBoost Ensemble Methods for Survivability with the Classification of Breast Cancer

Performance Analysis of XGBoost Ensemble Methods for Survivability with the Classification of Breast Cancer

AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease

Predictive Analysis of Mental Health Conditions Using AdaBoost Algorithm

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