Retracted: Estimation of Prediction for Getting Heart Disease Using Logistic Regression Model of Machine Learning

Saw, Montu; Saxena, Tarun; Kaithwas, Sanjana; Yadav, Rahul; Lal, Nidhi

doi:10.1109/iccci48352.2020.9104210

Cited by 54 publications

(19 citation statements)

References 7 publications

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“…On a Cleveland dataset, Detrano et al [ 20 ] achieved 77% using the logistic regression algorithm. Saw et al [ 21 ] have implemented the improved logistic regression classification model for heart disease dataset. The fast decision tree and C4.5 tree have been employed for HD prediction [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Karthick¹,

Aruna

Samikannu

et al. 2022

Computational and Mathematical Methods in Medicine

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Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset.

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Section: Related Workmentioning

confidence: 99%

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Karthick¹,

Aruna

Samikannu

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Sedangkan penelitian peneliti mengenai kasus SMS spam. Penelitian lainnya mengenai logistic regression adalah penelitian dari Montu Saw, Tarun Saxena, Sanjana Kaithwas, Rahul Yadav, dan Nidhi Lal (2020) [11]. Perbedaan penelitian ini adalah model evaluasi yang digunakan berupa Accuracy, Recall, Precision, dan F1-Score.…”

Section: Pendahuluanunclassified

Aplikasi Klasifikasi SMS Berbasis Web Menggunakan Algoritma Logistic Regression

Pramakrisna¹,

Adhinata²,

Tanjung³

2022

teknika

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Jenis SMS spam adalah jenis pesan teks yang tidak diinginkan atau tidak diminta yang dikirim ke ponsel pengguna, seringkali untuk tujuan komersial. Untuk mengatasi masalah spam, diperlukan teknik untuk memilah kata atau kalimat termasuk spam atau bukan spam. Pada penelitian ini diusulkan menggunakan machine learning untuk mengklasifikasikan pesan mana yang spam dan mana yang tidak spam. Data yang digunakan pada penelitian ini terdiri dari 1140 pesan, dimana sudah diberi label 0 untuk pesan yang tidak spam dan 1 untuk pesan yang spam. Algoritma yang digunakan untuk kasus ini adalah Logistic Regression. Hasil penelitian menunjukkan model memiliki tingkat akurasi untuk mengklasifikasi pesan, sebesar 97%. Aplikasi yang dikembangkan untuk menerapkan hasil pemodelan machine learning menggunakan bentuk sebuah website sederhana dengan bantuan Flask framework dari Python. Hasil akhir dari aplikasi ini adalah model machine learning yang dapat dibuka melalui website.

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“…In [122], the authors attempted to increase the accuracy of heart disease prediction by applying a Logistic Regression using a healthcare dataset to determine whether patients have heart illness problems or not.…”

Section: Ml-based Healthcare Predictionmentioning

confidence: 99%

Healthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A Survey

Badawy

Ramadan

Hefny

2022

Preprint

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Aim This paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction, as well as identify inherent obstacles to applying these approaches in the healthcare prediction domain. Background Healthcare prediction has been a significant factor in saving human lives in recent years. In the domain of healthcare, there is a rapid development of intelligent systems for analyzing complicated relationships among data and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry. Thus comes the role of systems depending on machine learning as well as deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Methods The studies discussed in this paper have been presented in journals published by IEEE, Springer, and Elsevier. Machine learning, deep learning, healthcare, surgery, cardiology, radiology, hepatology, and nephrology are some of the terms used to search for these studies. The studies chosen for this survey are concerned with the use of machine learning as well as deep learning algorithms in healthcare prediction. Results A total of 40 working papers were selected and the methodology for each paper was clarified. Conclusion This paper presents a comprehensive survey as well as the current challenges in healthcare prediction. studies have shown that artificial intelligence plays a significant role in diseases diagnosing.

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Retracted: Estimation of Prediction for Getting Heart Disease Using Logistic Regression Model of Machine Learning

Cited by 54 publications

References 7 publications

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Aplikasi Klasifikasi SMS Berbasis Web Menggunakan Algoritma Logistic Regression

Healthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A Survey

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