A Computational Intelligence Approach for Predicting Medical Insurance Cost

Hassan, Ch Anwar ul; Iqbal, Jawaid; Hussain, Saddam; AlSalman, Hussain; Mosleh, Mogeeb A. A.; Ullah, Syed Sajid

doi:10.1155/2021/1162553

Cited by 29 publications

(7 citation statements)

References 27 publications

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“…The author of [20] compared ML classifiers on various datasets such as heart and diabetes datasets. The authors of [21] examined ML classifiers on medical insurance cost datasets. [22] used six popular data mining tools to categorize heart disease: using LR, KNN, SVM, RF, and KNIME, these tools were compared to six commonly used machine learning techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

et al. 2022

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In medical filed, predicting the occurrence of heart diseases is a significant piece of work. Millions of healthcare-related complexities that have remained unsolved up until now can be greatly simplified with the help of machine learning. The proposed study is concerned with the cardiac disease diagnosis decision support system. An OpenML repository data stream with 1 million instances of heart disease and 14 features is used for this study. After applying to preprocess and feature engineering techniques, machine learning approaches like random forest, decision trees, gradient boosted trees, linear support vector classifier, logistic regression, one-vs-rest, and multilayer perceptron are used to perform binary and multiclassification on the data stream. When combined with the Max Abs Scaler technique, the multilayer perceptron performed satisfactorily in both binary (Accuracy 94.8%) and multiclassification (accuracy 88.2%). Compared to the other binary classification algorithms, the GBT delivered the right outcome (accuracy of 95.8%). Multilayer perceptrons, however, did well in multiple classifications. Techniques such as oversampling and undersampling have a negative impact on disease prediction. Machine learning methods like multilayer perceptrons and ensembles can be helpful for diagnosing cardiac conditions. For this kind of unbalanced data stream, sampling techniques like oversampling and undersampling are not practical.

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Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

et al. 2022

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“…A study by [26] used a diverse method of regression algorithms. This paper offers a computational intelligence technique for forecasting healthcare insurance costs.…”

Section: ) Regression Algorithmsmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms for Health Insurance Pricing

Bau,

Md Hanif

2024

JOIV : Int. J. Inform. Visualization

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Insurance is an effective way to guard against potential loss. Risk management is primarily employed to protect against the risk of a financial loss. Risk and uncertainty are inevitable parts of life, and the pace of life has led to a rise in these risks and uncertainties. Health insurance pricing has emerged as one of the essential fields of this study following the coronavirus pandemic. The anticipated outcomes from this study will be applied to guarantee that an insurance company's goal for its health insurance packages is within the range of profitability so that the insurance company will also choose the most price-effective course of action. The US Health Insurance dataset was utilized for this study. This health insurance pricing prediction aims to examine four different types of regression-based machine learning algorithms: multiple linear regression, ridge regression, XGBoost regression, and random forest regression. The implemented model's performance is assessed using four evaluation metrics: MAE, MSE, RMSE, and R2 score. Random forest regression outperforms all other algorithms in terms of all four evaluation metrics. The best machine learning algorithm, random forest, is further enhanced with hyperparameter tuning. Random forest with hyperparameter tuning performs better for three evaluation metrics except for MAE. To gain further insights, data visualizations are also implemented to showcase the importance of features and the differences between actual and predicted prices for all the data points.

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“…A confusion matrix is a tabular representation commonly employed in classification to assess the performance of a classification model. The confusion matrix provides a comprehensive breakdown of the model's predictions, offering insights into true positives, true negatives, false positives, and false negatives for each class in a multi-class scenario [88]. The confusion matrices of GRU, GB, LR, KNN, XGB, and RF are shown in Figures 7, 8, 9, 10, 11 and 12, respectively.…”

Section: Confusion Matrix Of ML and Dl Modelsmentioning

confidence: 99%

Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability

Alharbi,

Hassan

2023

Sustainability

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In the realm of sustainable IoT and AI applications for the well-being of elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications and extended periods of immobility on the floor. Researchers have been exploring various techniques for fall detection over the past decade, and this study introduces an innovative Elder Fall Detection system that harnesses IoT and AI technologies. In our IoT configuration, we integrate RFID tags into smart carpets along with RFID readers to identify falls among the elderly population. To simulate fall events, we conducted experiments with 13 participants. In these experiments, RFID tags embedded in the smart carpets transmit signals to RFID readers, effectively distinguishing signals from fall events and regular movements. When a fall is detected, the system activates a green signal, triggers an alarm, and sends notifications to alert caregivers or family members. To enhance the precision of fall detection, we employed various machine and deep learning classifiers, including Random Forest (RF), XGBoost, Gated Recurrent Units (GRUs), Logistic Regression (LGR), and K-Nearest Neighbors (KNN), to analyze the collected dataset. Results show that the Random Forest algorithm achieves a 43% accuracy rate, GRUs exhibit a 44% accuracy rate, and XGBoost achieves a 33% accuracy rate. Remarkably, KNN outperforms the others with an exceptional accuracy rate of 99%. This research aims to propose an efficient fall detection framework that significantly contributes to enhancing the safety and overall well-being of independently living elderly individuals. It aligns with the principles of sustainability in IoT and AI applications.

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A Computational Intelligence Approach for Predicting Medical Insurance Cost

Cited by 29 publications

References 27 publications

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

Comparative Analysis of Machine Learning Algorithms for Health Insurance Pricing

Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability

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