Classification techniques for Disease detection using Big-data

Shah, Jay; Patel, Raj Nath

doi:10.1109/iceeccot46775.2019.9114589

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…SVM is used for both regression and classification problems [44,45]. This approach works well for solving a problem in the form of a linear and nonlinear dataset [46,47]. SVM algorithm uses various kernel types like linear radial basis function (RBF), Polynomial, and Sigmoid for a prediction model [48].…”

Section: Support Vector Machinementioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

177

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Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

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Section: Support Vector Machinementioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

177

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“…It is composed of a single master node and seven slave nodes. The dataset of this experiment has 123-dimensional features to solve two Shah and Patel [16] proposed a new approach to diabetes datasets being categorized in a distributed environment. The suggested method is to apply the process of classification within Hadoop using Spark.…”

Section: Related Workmentioning

confidence: 99%

Parallel classification and optimization of telco trouble ticket dataset

2021

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“…The present study enhanced the classifier's performance in classifying a diabetes medical condition [4]. To address this concern [5], the majority of studies introduce feature selection techniques [6], which is a feature dimensionality reduction approach [7]. Several evolution techniques were used for feature selection subset search as a sort of optimization issue, which include particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) [8].…”

Section: Introductionmentioning

confidence: 99%

An Improved Homogeneous Ensemble Technique for Early Accurate Detection of Type 2 Diabetes Mellitus (T2DM)

Faustin

Zou

2022

Computation

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The objective of the present study is to improve the genetic algorithm (GA) supremacy in selecting the most suitable and relevant features within a highly dimensional dataset. This results in cost reduction and improving classification performance. During text classification, employing terms such as features using vector space representation can result in a high dimensionality of future space. This condition presents some issues, including high computation cost in data analysis and deteriorating classification accuracy performance. Several computational feature selection techniques can be applied in eliminating the least significant features within a dataset, including a genetic algorithm. The present study improved the performance of the classifier in classifying Pima Indian diabetes data. Despite the popularity of GA in the feature selection area, it does not provide the most optimal features due to one of its underlying issues: premature convergence due to insufficient population diversity in the future generations. GA was improved in its crossover operator using two steps: define a variable slice point on the size of the gene to be interchanged for every offspring generation and apply feature frequency scores in deciding the interchanging of genes. The above obtained results to the proposed technique will be better results than the results for standard GA. Our proposed algorithm attained an accuracy of 97.5%, precision of 98, recall of 97% and F1-score of 97%.

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Classification techniques for Disease detection using Big-data

Cited by 5 publications

References 5 publications

The Role of Machine Learning Algorithms for Diagnosing Diseases

The Role of Machine Learning Algorithms for Diagnosing Diseases

Parallel classification and optimization of telco trouble ticket dataset

An Improved Homogeneous Ensemble Technique for Early Accurate Detection of Type 2 Diabetes Mellitus (T2DM)

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