Evaluating the Efficacy of Diverse Machine Learning Techniques in Disease Detection: A Comparative Analysis

Detecti​ng diseases early is very important to help patients get results and lower healthcare expenses​s​. The field of machine learning has become highly valuable, in the healthcare industry as it can improve precision and effectiveness. In this research study​ we are examining how machine learning methods such, as Support Vector Machines (SVM) Random Forest (RF) Convolutional Neural Networks (CNN) Long Short Term Memory (LTSM) and Gradient Boosted Machines (GBMs) perform in detecting diseases. Information was gathered from Google Scholar regarding studies that utilized these models in the context of illnesses like diabetes and cardiovascular diseases well, as cancer and infectious diseases. The models were evaluated by considering their accuracy levels along with sensitivity and specificity measures in addition, to AUC‐ROC values. The findings reveal that Convolutional Neural Networks (CNN) achieved the level of accuracy at 99·54% along, with an AUC–ROC score of 99· RF models also showed strong performance with an accuracy of 96·74% and an AUC–ROC score of 99· showcasing their ability to handle extensive datasets effectively· Gradient Boosted Machines (GBMs) demonstrated high accuracy at 97·57% as well as a notable AUC–ROC score of 98· proving their effectiveness in predicting diabetes mellitus· Support Vector Machines (SVM) and Long Short Term Memory networks (LSTM) delivered reliable results as well; particularly noteworthy was the utility of LSTMs, in modeling temporal sequences for chronic disease monitoring purposes· The research points out the pros and cons of machine learning methods. Offers valuable information, on how they can be used in real life medical scenarios effectively. It indicates that incorporating ML models into the healthcare sector has the potential to boost detection of diseases and enhance treatment while also cutting down on medical expenses. Future studies should concentrate on broadening data sources to include a range of populations and establishing standardized assessment criteria to improve the reliability and practicality of ML models, in disease detection.