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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Non-invasive health prediction from visually observable features.

Fan Yi Khong¹, Tee Connie¹, Michael Kah Ong Goh¹

¹Faculty of Information Science and Technology, Multimedia University, Melaka, Melaka, 75450, Malaysia.

|May 10, 2022

Summary

This summary is machine-generated.

This study introduces a non-invasive machine learning approach using facial images for health prediction. This method offers a cost-effective way to assess health conditions, enhancing personalized healthcare services.

Keywords:

Health prediction Machine learning Remote screening and diagnosis

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Area of Science:

Artificial Intelligence in Healthcare
Machine Learning for Medical Diagnosis
Digital Health Screening

Background:

Artificial Intelligence (AI) is transforming healthcare, with self-diagnosis becoming key for personalized services.
Mobile health solutions are vital for reducing disease spread and overcoming geographical barriers, especially during pandemics.
Non-invasive health screening using visual features is an emerging area.

Purpose of the Study:

To present a novel, non-invasive screening approach for predicting a person's health status.
To utilize machine learning techniques for analyzing visually observable features from images.
To enable health predictions using readily available devices like cameras and mobile phones.

Main Methods:

A two-level hierarchical classification model was developed.
Binary classifiers were trained at each node of the hierarchy for class selection.
Prediction involved using a reduced feature set specific to each class.

Main Results:

Achieved testing accuracies of 86.87% for the first-level classification and 76.84% for the second-level classification.
Demonstrated favorable prediction outcomes with significantly reduced computational time.
Validated the effectiveness of the hierarchical approach in health prediction.

Conclusions:

It is feasible to predict health conditions using facial appearance with cost-effective machine learning.
The proposed method offers a promising avenue for accessible and efficient health assessments.
This approach supports the integration of AI in next-generation healthcare systems for personalized diagnostics.