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Review on Improved Machine Learning Techniques for Predicting Chronic Diseases

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Abstract

Healthcare industry is a stage which is presented with tremendous innovative headways consistently. Parkinson disease (PD) has become a critical overall general clinical issue starting late. To provide the solution for this problem, in this paper, use fusion of machine learning and federated learning techniques for processing electronically collected patients’ health record (PD dataset) in accurate manner. The PD dataset are constantly gathered and sorted out to give a point by point history of patients, their sicknesses and determination plans. The medical PD dataset contains 43 400 electronic records of potential patients which includes normal, Ischemic and Hemorrhagic stroke. Cleaning, finding feature correlation and imputing missing values in the PD has to be performed by preprocessing & normalization approach. For further processing, using Random over sampling (ROS) methods the imbalanced PD dataset will be converted into balanced. From the balanced PD datasets the stroke prediction accuracy will be validated using Decision Tree, Logistic Regression, Random Forest and Improved LSTM (Imp-LSTM) machine learning algorithms. Using distinct experiments of executing performance measurements the accuracy rate from our prediction classifiers for the patient with smokes category will be 62.29, 71.36, 96.51 and 99.56% respectively as like the patient with never smoked category dataset the accuracy will be 70.49, 75.86, 96.49 and 99.58% respectively. The proposed Imp-LSTM algorithm in this research will effectively produce high overall accuracy in both the datasets, which means a successful decrease in the misdiagnosis rate for stroke prediction.

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ACKNOWLEDGMENTS

This survey was supported by Karthikeyan J. I thank my supervisor from Vellore Institute of Technology, Vellore who provided invaluable insights and expertise that greatly assisted the research.

Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. School of Information Technology and Engineering, Vellore Institute of Technology (VIT), 632014, Tamilnadu, India

    L. Abirami & J. Karthikeyan

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  1. L. Abirami
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  2. J. Karthikeyan
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Correspondence to J. Karthikeyan.

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The authors of this work declare that they have no conflicts of interest.

FUTURE SCOPE

In future, the complete classification process can be improved by incorporating time threshold values into to all the gates of LSTM algorithm and enhance this with the advancement of soft computing technologies.

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Abirami, L., Karthikeyan, J. Review on Improved Machine Learning Techniques for Predicting Chronic Diseases. Opt. Mem. Neural Networks 33, 28–46 (2024). https://doi.org/10.3103/S1060992X24010028

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