Performance Analysis of Type-2 Diabetes Mellitus Prediction Using Machine Learning Algorithms: A Survey

Ahamed, B. Shamreen; Arya, Meenakshi S.; Nancy, V. Auxilia Osvin

doi:10.1007/978-981-19-2500-9_48

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…separates the data of a set of items into many sections based on comparable features. The error rate is computed based on the input and is determined using the logistic regression technique [40] [41]. The error rate calculation function is:…”

Section: Logistic Regressionmentioning

confidence: 99%

Classification and Diagnosis of Heart Disease Using Machine Learning

Mohsen‬‏,

Naoufel,

Alrashahy

et al. 2024

Preprint

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Heart disease is one of the most common causes of death worldwide, and accurate and rapid diagnosis is needed to avoid serious complications. This study aimed to present a system for diagnosing and classifying common heart diseases using machine learning techniques. The data used are a set of medical records for heart patients at Ibb Medical Clinic, Ibb City, Yemen. Fourteen different classification algorithms were implemented using the Weka tool to classify different heart diseases, including eight heart diseases. The results showed that the RF algorithm was the best in terms of accuracy, sensitivity, specification, and F-measure. Based on this algorithm, a web system based on the ASPX language was designed to help users enter patient data and obtain a diagnosis and classification of heart disease. The system can help doctors in the diagnosis process and increase the medical awareness of the community.

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Section: Logistic Regressionmentioning

confidence: 99%

Classification and Diagnosis of Heart Disease Using Machine Learning

Mohsen‬‏,

Naoufel,

Alrashahy

et al. 2024

Preprint

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show abstract

“…e equation represents the number of trees that can be used in the model depending on the number of instances used in the dataset. When compared with GB, LGBM is comparatively faster and the parameters used are different, which can further increase or decrease the efficiency [37].…”

Section: Light Gradient Boosting Machine (Lgbm)mentioning

confidence: 99%

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Over Sampling and Feature Augmentation

Ahamed,

Arya,

Nancy

2022

SSRN Journal

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Classification and Diagnosis of Heart Disease Using Machine Learning

Mohsen‬‏,

Naoufel,

Alrashahy

et al. 2024

Preprint

View full text Add to dashboard Cite

Heart disease is a common and serious disease that causes many deaths around the world. The study aims to explore the use of machine learning techniques in classifying and diagnosing heart diseases and to develop a system capable of diagnosing and classifying different types of heart diseases using machine learning techniques. A number of algorithms commonly used in healthcare, such as Naive Bayes model, SVM, k-nearest neighbor (k-NN), and others, were reviewed. The study points out the importance of the quality of the data used in the database to obtain an accurate and reliable diagnosis. Data were collected from patient records in hospitals and clinics, analyzed and compared with previous relevant studies. Clinical decision assistance software has been used to help make medical decisions based on patient information. Positive results have been achieved that confirm the effectiveness of using machine learning techniques in diagnosing heart diseases. These technologies have shown the potential to improve the accuracy and efficiency of diagnosis, leading to improved patient outcomes and reduced health burdens. It also concluded the need to develop effective diagnostic tools and enhance the prevention of heart disease. The study is an important foundation for healthcare professionals and doctors working in the field of cardiology, as the techniques used can help them better understand and diagnose conditions and improve patient care.

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Performance Analysis of Type-2 Diabetes Mellitus Prediction Using Machine Learning Algorithms: A Survey

Cited by 3 publications

References 39 publications

Classification and Diagnosis of Heart Disease Using Machine Learning

Classification and Diagnosis of Heart Disease Using Machine Learning

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Over Sampling and Feature Augmentation

Classification and Diagnosis of Heart Disease Using Machine Learning

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