Type II Diabetes Prediction Using Combo of SVM ANN and Random Tree

Alehegn, Minyechil; Joshi, Rahul

doi:10.35940/ijeat.f7974.088619

Cited by 11 publications

(8 citation statements)

References 14 publications

(14 reference statements)

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“…A pesar que 15 de los artículos no describen la herramienta utilizada en su contenido, 8 de ellos pertenecen a la base datos PIDD como por ejemplo (O. Barrios et al, 2017b), (Wu et al, 2018), (Alehegn y Joshi, 2019), (Shetty y Katkar, 2019b) donde dichos autores utilizan las 8 variables del conjunto para realizar la predicción a través de la validación cruzada utilizada comúnmente en la herramienta de software WEKA.…”

Section: Resultsunclassified

Estudio de algoritmos de inteligencia artificial más utilizados para el diagnóstico de diabetes mellitus tipo 2

Cuenca Andrade,

Guamán-Quinche,

Álvarez-Pineda

et al. 2023

CEDAMAZ

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La diabetes es la segunda causa de muerte a nivel mundial sobre todo en países de bajos recursos. En el Ecuador uno de cada diez personas es diagnosticado con diabetes mellitus tipo 2, esto es debido a factores de riesgo como: antecedentes familiares con diabetes, medicación, sedentarismo o mala alimentación. Por lo tanto, es indispensable hacer una Revisión Sistemática de Literatura sobre el estado del uso de técnicas o algorítmos de Inteligencia Artificial para el diagnóstico de diabetes mellitus tipo 2; con este fin de responder la pregunta: ¿Cuáles son las técnicas de inteligencia artificial aplicadas al diagnóstico de diabetes mellitus tipo 2?

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Section: Resultsunclassified

Estudio de algoritmos de inteligencia artificial más utilizados para el diagnóstico de diabetes mellitus tipo 2

Cuenca Andrade,

Guamán-Quinche,

Álvarez-Pineda

et al. 2023

CEDAMAZ

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“…The K-NN algorithm is a machine learning algorithm widely used for disease classification [6], one of which is diabetes. This research produces an accuracy of 74.59% [7] in another study predicting Atherosclerosis disease using the K-NN algorithm. The highest accuracy produced with the Hungarian dataset is 80% [8].…”

Section: Introductionmentioning

confidence: 86%

K Value Effect on Accuracy Using the K-NN for Heart Failure Dataset

Masitha,

Biddinika,

Herman

2023

matrik

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Heart failure is included in the category of cardiovascular disease. Heart disease is not easy to detect, and its detection needs to be done by experienced and skilled medical professionals. Most patients with heart failure require hospitalization. Common symptoms of heart disease, such as chest pain and high or low blood pressure, vary from person to person. This study aims to find the most optimal k value based on the accuracy obtained based on calculations by testing different k values, namely 1, 3, 5, 7, and 9. After getting the results of the accuracy of the five k values, compare which accuracy has the highest value, best for K-Nearest Neighbor (K-NN) models. The classification process uses the K-NN algorithm. This algorithm is quite easy to use because some parameters work using distance metrics and k values. Therefore, the value of k in the K-NN algorithm greatly affects the accuracy that will be produced. In the results of this study, the accuracy obtained was k = 7 and k = 9, which are the most optimal results because they have the highest accuracy compared to other k values, with an accuracy of 88%. The expected benefit of this research is that it can make a scientific contribution to research in the field of machine learning classification, especially in predicting heart failure

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“…The developed system achieved 93.62 percent accuracy in the case of PIDD and 88.56 percent accuracy for a large set of data from 130 hospitals in the United States. For large dataset analysis, the NB and J48 prediction algorithms were found to be superior [6].…”

Section: Related Workmentioning

confidence: 99%

Review Classification of Diabetes Using Machine Learning Technics

Elias,

Taher Jawhar

2024

IRJIET

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Diabetes is one of the major diseases of the population across worlds. Diabetes is a chronic disease that occurs either when the pan- crease does not produce enough insulin or when the body cannot efficiently use the insulin it produces. Diabetes, one of the top 10 causes of death worldwide, is associated with the interaction between lifestyle, psychosocial, medical conditions, demographic, and genetic risk factors. Predicting type 2 diabetes is important for providing prognosis or diagnosis support to allied health professionals, and aiding in the development of an efficient and effective prevention plan. Several works proposed machine-learning algorithms to predict type 2 diabetes. However, each work uses different datasets and evaluation metrics for algorithms’ evaluation, making it difficult to compare among them. Machine learning is one of the branches of artificial intelligence. It focuses on creating systems that learn data and acquire knowledge to improve their performance automatically and without using programming directly. Machine learning relies on algorithms and models that allow systems to analyze data, gain experience, and make decisions, enabling them to adapt to tasks and improve their performance with time passing. Machine learning algorithms have helped health professionals (including doctors) treat, analyze and diagnose medical problems, as well as detect disease patterns and other patient data. Machine learning can help people make an initial judgment about diabetes according to daily physical examination data, and can serve as a reference for doctors.

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Type II Diabetes Prediction Using Combo of SVM ANN and Random Tree

Cited by 11 publications

References 14 publications

Estudio de algoritmos de inteligencia artificial más utilizados para el diagnóstico de diabetes mellitus tipo 2

Estudio de algoritmos de inteligencia artificial más utilizados para el diagnóstico de diabetes mellitus tipo 2

K Value Effect on Accuracy Using the K-NN for Heart Failure Dataset

Review Classification of Diabetes Using Machine Learning Technics

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