Performance Comparison of Classification Algorithms on Medical Datasets

Ramana, Bendi Venkata; Boddu, Raja Sarath Kumar

doi:10.1109/ccwc.2019.8666497

Cited by 30 publications

(8 citation statements)

References 8 publications

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“…SVM provides a set of supervised learning models which enables our present study to perform classi cation and regression tasks that gives accurate prediction [7]. Support vectors have the information points that lie closest to the decision surface which are also called as hyper planes.…”

Section: Methodsmentioning

confidence: 99%

SARS-CoV-2 virion, debacle humane: An Analytical approach

Boddu¹

2023

Preprint

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World Health Organization (WHO) declared the coronavirus diseases COVID-19 is pandemic, as the virus SARS-CoV2, which causes coronavirus diseases (COVID-19) and it variants has spread all over the world. This pandemic disease, COVID-19 is primarily described as an infectious disease-causing severe acute respiratory syndrome (SARS). Later it transforms into severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virion variant. These virion variants have emerged across the globe with deceptively higher transmissibility and with immunity deteriorate capabilities. In this research paper, it is proposed to compare several ML algorithms to predict the COVID-19 mortality using the various countries data and choose the best performing algorithm as a predictive tool for decision-making. This study aimed to develop a mortality risk prediction for COVID-19 based on ML algorithms that utilize data.

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Section: Methodsmentioning

confidence: 99%

SARS-CoV-2 virion, debacle humane: An Analytical approach

Boddu¹

2023

Preprint

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“…In the literature, some authors like Kumar and Sahu [119], Auxilia [20], Ramana and Boddu [174] and Srivenkatesh [210] emphasized the comparative performance of various classifiers on the ILPD. Kumar and Sahu [119] performed a set of experiments to check the performance of classifiers like RF, CART and Reduced Error Pruning Tree (REPTree) with two different train test splits: 70%-30% and 80%-20%, and inclusion/exclusion of FS technique.…”

Section: Liver Disease Prediction Methodsmentioning

confidence: 99%

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

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“…In this, the mean is used to handle the missing values. Ramana and Boddu [39] used the naïve Bayes classification algorithm, and the accuracy achieved was 76.34%. Balajiet al [40] used a deep NN restricted Boltzmann machine, and 80.9% accuracy was achieved.…”

Section: Related Workmentioning

confidence: 99%

A Novel Architecture for Diabetes Patients’ Prediction Using K-Means Clustering and SVM

Arora

Singh

Al-Dabagh³

et al. 2022

Mathematical Problems in Engineering

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Diabetes is one of the alarming issues in today’s era. It is a chronic disease that may cause many health-related problems. It is a group of syndrome that results in too much sugar in the blood. Diabetes’s chronic hyperglycemia has been linked to long-term damage, organ breakdown, and organ failure, notably in the eyes, kidneys, nerves, heart, and veins. Machine learning has quickly advanced, and it is now used in many facets of medical health. The goal of this research is to create a model with the highest level of accuracy that can predict a patient’s chance of developing diabetes. This paper proposes a novel architecture for predicting diabetes patients using the K-means clustering technique and support vector machine (SVM). The features extracted from K-means are then classified using an SVM classifier. A publicly available dataset, namely, the Pima Indians Diabetes Database, is tested using this approach. Accuracy of 98.7% is noted on the used dataset. On this dataset, the combined method performs better than the conventional SVM-based classification. This paper also compared the accuracy, precision, recall, and F1-score of the different machine learning techniques for classifying diabetes patients.

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Performance Comparison of Classification Algorithms on Medical Datasets

Cited by 30 publications

References 8 publications

SARS-CoV-2 virion, debacle humane: An Analytical approach

SARS-CoV-2 virion, debacle humane: An Analytical approach

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

A Novel Architecture for Diabetes Patients’ Prediction Using K-Means Clustering and SVM

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