Improved Automatic Feature Selection Approach for Health Risk Prediction

Gajare, Shreyal; Sonawani, Shilpa

doi:10.1109/iccmc.2018.8487695

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…The healthcare domain might be considered as a rich data source as it produces high amounts of underutilized data involving administrative reports, standard findings and electronic clinical records [23]. A detailed analysis on the different data mining techniques employed in the healthcare field [24][25][26] was done with respect to the type of disease predicted, preprocessing technique used, datasets used and the performance analysis factors.…”

Section: Related Studiesmentioning

confidence: 99%

Hybrid Multi Cloud based Disease Prediction Model for Type II Diabetes

Durgadevi,

Kalpana

2020

IJEAT

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Advancements in health informatics pave the way to explore new medical decision making systems which are characterized by an exponential evolution of knowledge. In the medical domain, disease prediction has become the centre of research with the increasing trend of healthcare applications. The predictive knowledge for the diagnosis of disease highly depends on the subjective knowledge of the experts. So the development of a disease prediction model in time is essential for patients and physicians to overcome the problem of medical distress. This paper explores a hybrid approach (Cooperative Ant Miner Genetic Algorithm) for classifying the medical data. Three benchmarked Type II diabetic datasets (US, PIMA, German) from the UCI machine learning repository were used to analyze the effectiveness of the disease prediction model. The devised classification algorithm with a Soft-Set approach was deployed in a Multi-Cloud environment for enhancing the storage and retrieval of data with reduced response and computation time. The cooperative classification algorithm in the cloud database distinguishes the diseased cases from the normal ones .The soft set theory analyzes the severity of the diseased cases by calculating the percentage of diabetic risk using soft intelligent rules and stores them in a separate knowledge base. Thus the proposed model serves as a suitable tool for eliciting and representing the expert’s decision which aids in prediction of Type II diabetic risk percentage leading to the timely treatment of patients.

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Section: Related Studiesmentioning

confidence: 99%

Hybrid Multi Cloud based Disease Prediction Model for Type II Diabetes

Durgadevi,

Kalpana

2020

IJEAT

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“…The predictive algorithms can be divided into classification and regression based on the type of target variable (discrete or continuous). The descriptive algorithms can be classified into clustering and association-based rule mining (Gajare and Sonawani, 2018).…”

Section: Data Modellingmentioning

confidence: 99%

“…Growth of information storage technology in DM involves two aspects namely; algorithm development and the rise of modern storage equipment which generates huge amount of raw data (Devi et al, 2016). The characteristics of extracted knowledge such as time complexity, comprehensibility and accuracy can be used as a new knowledge (Gajare and Sonawani, 2018). The stages in DM involves:…”

Section: Introductionmentioning

confidence: 99%

A cooperative GA-SM-based prediction model for healthcare services

Durgadevi

Kalpana

2020

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Diabetes mellitus is a major health challenge around the world. The blood glucose level is one of the major factors in the human body and a significant increase in its level can cause many harmful effects in human life. It is expected that early diagnosis of diabetes mellitus can lead to rapid and effective treatment of glycemic control. As the number of people who suffer from diabetes mellitus increases significantly, a study on diabetes mellitus prediction was done through well-known methods in data mining (DM). In this paper, a genetic algorithm (GA)-based suppressor mutation (SM) optimisation rule miner has been proposed as a cooperative approach for prediction of diabetes mellitus. A novel fitness function has been incorporated into the GA-SM approach to generate a comprehensive optimal rule set while balancing accuracy, sensitivity and specificity. The proposed rule miner was compared against three rule-based algorithms, namely CN2, J48 and BF tree on the Pima Indians Diabetes Dataset with 768 patient records using tenfold cross validation. The results obtained prove that the proposed GA-SM approach has outperformed CN2, J48 and BF tree with respect to accuracy and kappa.

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Improved Automatic Feature Selection Approach for Health Risk Prediction

Cited by 2 publications

References 8 publications

Hybrid Multi Cloud based Disease Prediction Model for Type II Diabetes

Hybrid Multi Cloud based Disease Prediction Model for Type II Diabetes

A cooperative GA-SM-based prediction model for healthcare services

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