Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Author: search.filters.author.Leela Akshaya, T.Has files: No

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Predicting Survival of People with Heart Failure Using Oversampling, Feature Selections and Dimensionality Reduction
    (Institute of Electrical and Electronics Engineers Inc., 2022) Niharika, G.; Lekha, A.I.; Leela Akshaya, T.; Anand Kumar, A.M.
    Cardiovascular diseases are deadly and kill millions of people around the world every year. Heart failure is one of the unfortunate consequence where the heart is unable to pump enough blood for the body. A medical checkup of these patients with attributes including creatinine phosphokinase, ejection fraction, serum creatinine and serum sodium can be used for analysis. In this paper, we have analysed this clinical data and built machine learning models that can predict the survival rate of heart failure of a person. We have used various dimensionality reduction techniques to analyse the data with the aim of reducing the dimensions of the dataset. Finally, we reduced the overfitting of data using Synthetic Minority Oversampling Technique(SMOTE) and Adaptive Synthetic(ADASYN). © 2022 IEEE.
  • No Thumbnail Available
    Item
    Automated Health Insurance Management Framework with Intelligent Fraud Detection, Premium Prediction, and Risk Prediction
    (Springer Science and Business Media Deutschland GmbH, 2024) Devaguptam, D.; Gorti, S.S.; Leela Akshaya, T.; Sowmya Kamath, S.
    Private insurance is already one of the sectors with the greatest growth potential. For the majority of high-value assets today, including houses, jewelry, cars, and other valuable items, there are insurance solutions available. To maximize profits while handling client claims, insurance firms are leading have adopted cutting-edge operations, procedures, and mathematical models for estimating risks and serving customer best interests, while also maximizing profits. In this work, we aim to develop a machine learning-based automated framework that minimizes human involvement, protects insurance operations, identifies high-risk consumers, uncovers false claims, and lowers financial loss for the insurance industry. This framework consists of fraud detection followed by risk prediction and premium prediction. We trained and tested different machine learning approaches for each of the three insurance processing tasks; the observations are presented in this article. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.