• Rajeev Rajput
• Dr Jitendra Agrawal
• Prof . Sumit Sharma

In this research work present a hybrid model that is combination of linear regression model and cascaded feed forward network with back propagation training. The detection of the diabetic type 2 patient shows better accuracy as compare to other previous methods. In the different previous method analysis got the drawbacks of previous method that is over come in the proposed method that is shown in the result discussion. The outcome proposed method shown that can be detected with an awfully high accuracy, up to 96.54 % in the different diabetic patient datasets. The proposed method also shows lower complexity and better timing due to regression model soft threshold values. In the proposed hybrid model is the combination of CFFNN and Linear Regression method. Cascade-forward networks are similar to feed-forward networks, but include a connection from the input and every previous layer to following layers. As with feed-forward networks, a two-or more layer cascade network can learn any finite input-output relationship arbitrarily well given enough hidden neurons.For greater accuracy on low-dimensional through medium-dimensional data sets, fit a linear regression model.

Diabetic Type 2, Cascaded Feed Forward Network , Linear Regression, Machine Learning Processes, Random Forent And Accuracy

"Type II Diabetes Mellitus Analysis Based On Linear Regression and Cascade Forward Neural Network (CFNN) ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 3, page no.568-573, March-2021, Available :http://www.jetir.org/papers/JETIR2103079.pdf

"Type II Diabetes Mellitus Analysis Based On Linear Regression and Cascade Forward Neural Network (CFNN) ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 3, page no. pp568-573, March-2021, Available at : http://www.jetir.org/papers/JETIR2103079.pdf