Abstract:
This study offers a thorough methodology that makes use of a variety of deep learning and
machine learning algorithms to predict early stage of diabetes. Recurrent neural networks
(RNN), Feedforward Neural Networks (FNN), Decision Trees (DT), Logistic Regression
(LR), Random Forests (RF), K-Nearest Neighbors (KNN), Naive Bayes (NB), Support
Vector Machines (SVM), and Long Short-Term Memory (LSTM) are all integrated in the
suggested system. Nine health attributes for 100,000 entries are included in the dataset,
which was obtained via Kaggle. Exploratory data analysis, quality checks, and encoding
are all part of data pre-processing. For model evaluation, the dataset is divided into training
and test sets, and a two-pronged feature selection technique is used. Notably, with 97%
accuracy, the Decision Tree machine learning model shows greater accuracy in diabetes
prediction. The study places a strong emphasis on moral issues with predictive modeling
in healthcare. Prospective avenues for investigation encompass improving prediction
models, augmenting openness, and tackling wider ethical considerations in the field of
healthcare analytics.
