Improved ML Algorithms for Medical Image Analysis, Disease Diagnosis and Patient Risk Prediction in Health Care

Author(s)

Pradeep KGM, Dasari Rammurthy, Mathesh G

Abstract

In the field of healthcare, data mining techniques hold significant promise for enhancing patient care, disease prevention, and healthcare management. Complex data mining methods, like deep learning, can lead to overfitting and produce difficult-to-interpret black-box models. The most challenging Android assistance application to locate nearby hospitals emerges as a crucial, potentially life-saving endeavour and many more features are included. the MIMIC-III dataset offers a rich source of de-identified health data from Beth Israel Deaconess Hospital in Boston, Massachusetts, encompassing electronic health records, vital signs, lab results, and more. Quality-based images and some DL techniques are utilized for this kind of application. The process involves collecting and preprocessing medical images, including segmentation to isolate regions of interest. Regarding RP methodologies, RF stands out as a versatile and robust option, capable of handling diverse data types while mitigating overfitting risks. While SVM, GBM, and NN also have their strengths, RF's interpretability, ease of training, and reliability make them suitable for various risk prediction tasks. Appropriate algorithms are applied for analysis, and performance is evaluated using validation techniques.

Keywords

Machine Learning, Risk Prediction, Image Analysis, Medical Diagnosis, Correlation

References

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Article Details

Improved ML Algorithms for Medical Image Analysis, Disease Diagnosis and Patient Risk Prediction in Health Care

Author(s)

Abstract

Keywords

References

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