HepatitisC Classification using Data Mining Techniques

Yasin, Huda; Jilani, Tahseen Ahmed; Danish, Madiha

doi:10.5120/2934-3888

Cited by 16 publications

(8 citation statements)

References 3 publications

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“…Study identifies stroke severity indices, which represent proxy measures of neurologic impairment that could be used for ischemic stroke patient to adjust strokes severity. Easton et al [145] [147]. Zayed et al [148] used decision tree (C4.5) to predict therapeutic outcome to antiviral therapy in HCV patients.…”

Section: Classificationmentioning

confidence: 99%

Big data analytics for preventive medicine

Razzak

Imran

2019

Neural Comput & Applic

158

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Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

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Section: Classificationmentioning

confidence: 99%

Big data analytics for preventive medicine

Razzak

Imran

2019

Neural Comput & Applic

158

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“…Hepatitis is a dangerous and transmissible disease [1][2][3][4]. The virus of this disease can spread from one infected person to another healthy human being.…”

Section: Introductionmentioning

confidence: 99%

Treatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques

et al. 2021

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The proper prognosis of treatment response is crucial in any medical therapy to reduce the effects of the disease and of the medication as well. The mortality rate due to hepatitis c virus (HCV) is high in Pakistan as well as all over the world. During the treatment of any disease, prediction of treatment response against any particular medicine is difficult. This paper focuses on predicting the treatment response of a drug: “L-ornithine L-Aspartate (LOLA)” in hepatitis c patients. We have used various machine learning techniques for the prediction of treatment response, including: “K Nearest Neighbor, kStar, Naive Bayes, Random Forest, Radial Basis Function, PART, Decision Tree, OneR, Support Vector Machine and Multi-Layer Perceptron”. Performance measures used to analyze the performance of used machine learning techniques include, “Accuracy, Recall, Precision, and F-Measure”.

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“…There have been a rapid and massive increase in amount of data accumulated in recent years, due to this, the use of clustering has also expanded further, in its applications such as personalization and targeted advertising. Clustering is now a key component of interactive-systems which gather information on millions of users on everyday basis [1][2][3][4][5][6][7][8][9][10]20]. A process of dividing, classifying or grouping a dataset into meaningful similar partitions or subclasses based on some criteria, normally a distance function between objects, called clusters.…”

Section: Introductionmentioning

confidence: 99%