Decision support system for medical diagnosis using a kernel-based approach

Mezrigui, Houda; Theljani, Foued; Laabidi, Kaouther

doi:10.1109/cadiag.2017.8075675

Cited by 9 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combined techniques, including RF [24], that might tackle problems such as imbalanced datum points, pruning, and accuracy, were used to disprove numerous DT justifications. t is said that RF may replace the highly accurate ML method, but it also takes away the DT's compressing abilities.…”

Section: Assessment Of Known Learning Algorithmsmentioning

confidence: 99%

Untitled

2024

INDJCSE

View full text Add to dashboard Cite

Heart disease is one of the most challenging tasks in health care. Several works have been proposed to address this challenge. In the recent past, some of the research articles are focused on the design of different machine learning algorithms for heart disease in health care, in this paper, we present an overview of supervised, unsupervised, and reinforcement learning algorithms used in the prediction of heart disease wherever possible. This review paper classifies some machine learning algorithms into different categories named Naïve Bays, Decision trees, support vectors, KNN, random forest, genetic algorithms, and structures with their relative performances. This paper also compares different algorithm techniques for heart diseases against accuracy and reliability.

show abstract

Section: Assessment Of Known Learning Algorithmsmentioning

confidence: 99%

Untitled

2024

INDJCSE

View full text Add to dashboard Cite

show abstract

“…[5].In [9], SVM performs the best with 85.7655% of correctly classified instance and in [10] SVM is used with boosting technique to give an accuracy of 84.81%. HoudaMezrigui et al have used SVM to attain a f-measure value of 93.5617 [11]. In [12] SVM classifies the pixel variation with an accuracy of 92.1% helping to identify the affected region accurately.…”

Section: B Support Vector Machinementioning

confidence: 99%

Heart disease prediction using machine learning techniques : a survey

Ramalingam¹,

Dandapath²,

Raja³

2018

IJET

226

View full text Add to dashboard Cite

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

show abstract