Abstract

The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system's performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system's improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.

Highlights

  • A recent development in biotechnologies and high throughout computing progressively contribute to quick and affordable e-healthcare data collections and disease diagnosis.e efficiency and reliability are dependent on accurate model building from e-healthcare big data

  • Diabetes disease arrests 422 million adults all over the world [4]. e death rate due to diabetes disease is 1.5 million, and 3.7 million deaths are due to diabetes and high blood pressure [4]

  • With the intelligent recommendation system’s help and application of deep machine learning (DML) and artificial intelligent methods, this disease can be predicted at the earlier stage [5,6,7,8,9] with a minimal error rate

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Summary

Introduction

A recent development in biotechnologies and high throughout computing progressively contribute to quick and affordable e-healthcare data collections and disease diagnosis.e efficiency and reliability are dependent on accurate model building from e-healthcare big data. The ensemble deep learning model structure is presented, and it is further deployed in the whole SHRS-M3DP to predict and recommend diabetes disease in the patients. E validation phase comes in the last where trained fused SHRS-M3DP model is imported for prediction to authenticate whether the patient is affected with multidisciplinary diabetes disease based on the results.

Results
Conclusion
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