Summary of A Comparative Study on Machine Learning Models to Classify Diseases Based on Patient Behaviour and Habits, by Elham Musaaed et al.
A Comparative Study on Machine Learning Models to Classify Diseases Based on Patient Behaviour and Habits
by Elham Musaaed, Nabil Hewahi, Abdulla Alasaadi
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel study employs six supervised machine learning (ML) approaches to investigate the correlation between patient-related factors (PRF) and four diseases: Diabetes, Stroke, Heart Disease (HD), and Kidney Disease (KD). The research aims to identify disease risk factors and their interactions using historical data analysis, ultimately contributing to accurate disease diagnosis and proactive awareness. Six supervised ML algorithms are compared and evaluated for classifying diseases based on PRF, with a focus on HD predictions through a web-based application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers is working on a project that uses special computer programs called machine learning (ML) to help doctors diagnose diseases more accurately. They’re looking at a lot of information about patients, like their height, weight, age, and other health details. This data can help identify risk factors for certain diseases, like diabetes, stroke, heart disease, and kidney disease. The goal is to develop ways to predict these diseases using ML algorithms and provide a tool for people to check their own risk levels. |
Keywords
» Artificial intelligence » Machine learning » Supervised
