Comprehensive Predictive Health Diagnosis System using Machine Learning

Resumen

The paper proposes the Health Diagnosis System with machine-learning algorithms that
make use of available datasets taken from Kaggle. The data sets have records on diabetes, heart
diseases, and Parkinsons that forming a basis on which to predict models. There are two support
vector machine (SVM) programs model for the diabetes and Parkinson's cases, the other one
logistic regression supplies the heart-health warnings. The mixing of these algorithms allow each
disease to be treat as it fine deserves and improving overall accuracy and fostering that can be used
in real clinics. The work is found at the intersection of technologies vis-a-vis medicine and that can
demonstrate how the use of data can have advance prevention. By integrating the user-friendly ML
algorithms like the DT algorithm with more sophisticated ones are SVM and Logistic Regression.
Therefore, an open-source platform Streamlet is used as the IT interface to host. The greater
accessibility and usability of a predictive system can be easier. The technological integration
displayed in this paper is pioneering towards a healthcare system and rendering it more resilient
and responsive, fostering for better early diagnosis and thus proactive management of prevalent
health conditions.