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Improved logistic regression approach in feature selection for EHR
Published in Springer Verlag
Volume: 940
Pages: 325 - 334
Nowadays, population is growing on large scale along with the problems faced by the people are also increasing. Thus, healthcare industry is making lot of technological advancements to provide effective, faster and cheaper treatment to people. Digitization of health records are also expanding in zettabytes. Electronic Health Record (EHR) containing all the patient’s medical history, demographics and other clinical data is also used in hospitals for improved care co-ordination. To avoid critical conditions of people from chronic diseases like hypertension, diabetes, hyperlipidemia etc. there is a need for building a health risk prediction model. But, when whole EHR data is provided to this risk prediction model causes overfitting of features. Overfitting is caused when model learns the details & noise from dataset, thus having negative impact on the performance. Hence, a feature selection approach is proposed for discarding redundant features from EHR. Improved sparse logistic regression method selects the best suitable parameters and forwards to risk prediction model. This regression method improvises the model with the use of logistic loss function that controls the sparsity factor. Neural network is used as a risk prediction model. This paper describes the risk prediction of hypertension disease. Thus, people could take preventive measures for the disease. © Springer Nature Switzerland AG 2020.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer Verlag