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Diabetes is a very common disease that affects people all over the world. Diabetes raises the risk of long-term complications such as heart disease and kidney failure. If this disease is detected early, people may live longer and healthier lives. Different supervised machine learning models trained on appropriate datasets can aid in the early detection of diabetes. The goal is to develop effective machine-learning-based classifier models for detecting diabetes in people using clinical data. K-nearest neighbour (KNN), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine are among the machine learning algorithms that will be trained using various datasets (SVM).We used efficient pre-processing techniques to improve the model's accuracy. Furthermore, we identified and prioritised a number of risk factors using various feature selection approaches. Extensive experiments have been carried out to evaluate the model's performance on various datasets. When our model is compared to some recent studies, the results show that the proposed model can provide better accuracy ranging from 2.71 percent to 13.13 percent depending on the dataset and ML algorithm used. Finally, the most accurate machine learning algorithm is chosen for further development. We use the Python Flask web development framework to integrate this model into a web application. The findings suggest that using an appropriate preprocessing pipeline on clinical data and applying ML-based classification can accurately and efficiently predict diabetes.
Keywords:
Decision Support Systems, Diabetes prediction,Machinelearning,SupportVectorMachine, RandomForest, K-NearestNeighbor,LogisticsRegression
Cite Article:
"Diabetes Prediction Using Machine Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 7, page no.863 - 870, July-2022, Available :http://www.ijrti.org/papers/IJRTI2207128.pdf
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000204899
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator