Soft Computing based Approaches for Classifying Diseases using Medical Diagnosis Dataset

Aher, Chetan Nimba; Jena, Ajay Kumar

doi:10.1109/esci48226.2020.9167518

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…If the pheromone constraints are selected without expertise and theoretical justifications, the results obtained are likely to be greatly dispersed because of the algorithm's inefficiency and poor convergence. Aher et al [22] proposed a technique for cancer classification using Rider Optimization algorithm and Chicken Swarm Optimization for rapid diagnosis of disease severity evaluation implementing microarray innovation. Microarray innovation is a hybridization based strategy that can gauge the articulation levels of thousands of qualities in a solitary examination.…”

Section: Related Workmentioning

confidence: 99%

Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

Dhruv¹,

Mittal²,

Modi³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Pancreatic cancer is highly lethal as it grows, spreads rapidly and difficult to diagnose at its early stages. It can be identified through scan images. The tumorous images obtained from imaging techniques suffer from the drawback of cryptic data due to presence of unwanted noise and poor contrast. OBJECTIVE: To reduce the risk of pancreatic cancer, its detection and diagnosis at an early stage becomes crucial. METHODS: The proposed work encompasses the processing of CT scans of pancreatic tumor using classical and artificial intelligence based optimized edge detection techniques for optimization and detection of tumor. RESULTS: The simulation results are highly encouraging as evident from the far improved visibility of resultant images with Particle Swarm Optimization. CONCLUSION: The output image with PSO shows the quality enhanced CT images which helps in accurate detection and diagnosis of the pancreatic tumor at an early stage providing an aid in medical imaging.

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Section: Related Workmentioning

confidence: 99%

Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

Dhruv¹,

Mittal²,

Modi³

2021

EAI Endorsed Trans Perv Health Tech

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“…Artificial neural network, support vector machine, decision tree and Naïve Bayes have been used in prediction of cancer [13]. A study [1] have used similar dataset to present a framework for rapid prediction of heart disease using adaptive boosting algorithm and its classifiers.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Ensemble Model for Rapid Prediction of Heart Disease

Harika

Swamy²,

Nilima

2021

SN COMPUT. SCI.

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Heart disease is the leading cause of mortality among men and women. Accurate and rapid diagnosis of heart disease will assist in saving many lives. To develop a novel ensemble framework based on heterogeneous classifiers namely support vector machine (SVM), Naïve Bayes (NB), and artificial neural networks (ANN) for rapid prediction of heart disease. The present study also verifies the most accurate algorithm among all three. Data are collected from the UCI machine learning repository. After pre-processing, the data were divided into training and test data in a ratio of 80:20. Using the training data, the three contributing algorithms were trained by providing heart disease status. The algorithms were tested with the unseen data instances and hence evaluated for accuracy. The ensemble technique uses the results from individual classifiers and yields a result based on majority voting method. The ensemble model was observed to predict heart disease with an accuracy of 87.05% followed by ANN (84.74%), NB (81.35%) and SVM (79.66%). Among the individual classifiers, ANN had the least miss-classification rate and performed best in terms of all other model diagnostics. The use of the proposed ensemble classifier is recommended to predict the heart condition to have better accuracy and least miss-classification.

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Heart Disease Detection Through Deep Learning Model RNN

Kamble,

Kale,

Nitture

et al. 2022

Smart Intelligent Computing and Applications, Volume 2

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Soft Computing based Approaches for Classifying Diseases using Medical Diagnosis Dataset

Cited by 9 publications

References 19 publications

Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

Artificial Intelligence-Based Ensemble Model for Rapid Prediction of Heart Disease

Heart Disease Detection Through Deep Learning Model RNN

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