Health Detection Using Machine Learning

Abstract

The rapid increase in patient data and the growing demands on healthcare systems have made early identification and prioritization of critical cases a complex and time-sensitive challenge, often resulting in delays in diagnosis, treatment, and overall patient care. Traditional methods of analysing medical reports rely heavily on manual interpretation by healthcare professionals, which can be time-consuming, error-prone, and inefficient, especially in high-pressure environments with large patient volumes. To address these limitations, this paper proposes a machine learning–based health risk detection and rapid alert system designed to automate the analysis of patient medical reports and assist in effective clinical decision-making. The system extracts key clinical features such as vital signs, laboratory test results, and patient medical history, and processes them using supervised machine learning algorithms including Random Forest, Decision Trees, and Logistic Regression to classify patients into risk categories such as low, medium, and high risk. Advanced data pre-processing techniques such as data cleaning, normalization, feature selection, and handling of missing values are applied to enhance the accuracy and reliability of the model. Once the analysis is completed, the system generates real-time alerts for high-risk patients, enabling immediate medical intervention and significantly reducing response time in critical situations. By automating the initial screening process, the proposed system reduces the workload on healthcare professionals, minimizes human errors, and ensures that no critical case is overlooked. Additionally, it improves hospital workflow efficiency and supports better resource allocation by helping medical staff focus on patients who require urgent care. Experimental evaluations indicate that the system achieves high accuracy, consistency, and reliability across different datasets, demonstrating its potential for real-world implementation.

Introduction

The text presents a machine learning–based health risk detection and real-time alert system designed to improve patient prioritization in healthcare environments. With the rapid growth of electronic health records and clinical data, hospitals face challenges in quickly identifying high-risk patients, especially when manual analysis is time-consuming and prone to errors. This can delay treatment and reduce the quality of care.

To address this, the proposed system uses machine learning to automatically analyze patient data such as vital signs, lab results, and medical history, and classifies patients into low, medium, and high-risk categories. High-risk patients trigger immediate alerts to healthcare professionals, enabling faster intervention and improving clinical outcomes.

The literature review highlights existing work using machine learning and deep learning for disease prediction and clinical decision support, including studies by Rajkomar, Deo, Kavakiotis, Esteva, and Chen. While these approaches show strong performance in disease prediction, most focus on individual diseases and lack integrated patient prioritization, multi-level risk classification, and real-time alert systems.

The proposed system addresses these gaps by providing an end-to-end clinical decision support pipeline that includes data preprocessing, feature extraction, machine learning-based classification, and automated alert generation.

Two datasets are used for evaluation:

• UCI Heart Disease dataset
• Pima Indians Diabetes dataset

Data preprocessing includes cleaning, handling missing values, normalization, and feature selection using Random Forest importance scores.

The system evaluates multiple machine learning models, including:

• Random Forest
• Decision Tree
• Logistic Regression

Among these, Random Forest performs best due to its ensemble nature and ability to handle non-linear relationships in clinical data.

Finally, the system generates real-time alerts based on risk levels:

• High-risk patients receive immediate notifications
• Medium-risk patients are monitored closely
• Low-risk patients follow standard care procedures

References

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Copyright

Copyright © 2026 Prajan Shakthi S, Vishvesh C, Yokesh V, Mrs. Lekha P. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.