Publication Date: 2023/12/08
Abstract: Diabetes has been recorded as a serious glob- al health issue today. It's a long-term metabolic disease that takes place when blood glucose levels elevate in the human body. Early and accurate diabetes diagnosis is essential for managing the condition precisely and will prevent complications quickly. This count proposes a comprehensive and effective machine-learning method for detecting and treating diabetes. The dataset that was used contains many clinical and demographic variables such as age, BMI, family history and various blood test results. To identifythe most relevant variables, the tech- nique prioritizes the data to control for missing values and to normalize features. The next stepis to go through a strict feature selection process. For the trainingand vali- dation of the model, SVM, RFM, Logistic Regression, and Support Vector Machines (SVM) are just a few of the machine learning algorithms that are employed. The performance of each of these algorithms is checked using metrics like accuracy, redundancy, uniqueness, and re- ceiver operating characteristic (ROC) curve area. An en- semble perspective is also explored to combine thebenefits of multiple models and increase overall predicting power. The recommended model is tested on various test da- tasets for assessment purposes of its generalizability. The main purpose of the project is to create a robust and trustworthy diabetes detection tool that can be used in clinical settings to aid medical professionals with ad- vanced diagnosis and individualized treatment planning. The results demonstrate growing performance and the potential for machine learning to increase diabetes de- tection accuracy. The importance of the proposed model to subtle patterns in different patient data sets suggests that it could apply to a large range of demographics. This work lays the root level for future analysis into en- hancing and expanding the capabilities of diabetesdetec- tion models, which will advance ongoing efforts to apply machine learning to healthcare applications.
Keywords: Diabetes, Early Diagnosis, Machine Learning, Ac-Curacy, Healthcare Applications.
DOI: https://doi.org/10.5281/zenodo.10319575
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23NOV2342.pdf
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