Prediction of diabetes Using an Artificial Neural Network
Mayra Lachhani, Kumar, Muhammad Sapon, Khadijah Akmal, Suehazlyn Ismail, Zainudin, Parastoo Rahimloo, Ahmad Jafarian, Suyash Srivastava, Lokesh Sharma, Vijeta Sharma, Ajai Kumar, Hemant Darbari, Deepti Sisodia, Dilip Singh, Sisodia, William Sandham, Dimitra Nikoletou, David Hamilton, Ken Paterson, Alan Japp, Catriona Macgregor, Talha Alam, Muhammad Mahboob, Yasir Atif Iqbal, Abdul Ali, Safdar Wahab, Ijaz, Imtiaz Talha, Ayaz Baig, Hussain, Tejas Joshi, P Chawan, Somnath Rakshit, Suvojit Manna, Sanket Biswas, Riyanka Kundu, Priti Gupta, Sayantan Maitra, Subhas Barman, Sneha Joshi, Megha Borse, Yogender Aggarwal, Joyani Das, Mitra Papiya, Rohit Mazumder, Rakesh Kumar, Sinha, Meng Hsieh, Li-Min Hsuen, Cheng-Li Sun, Meng-Ju Lin, Chung-Y. Hsieh, Chia-Hung Hsu, Kao, Lily Tapak, Hossein Mahjub, Omid Hamidi, Jalal Poorolajal, Huy Pham, Anh Nguyen, Evangelos Triantaphyllou, Rajeeb Dey, Vaibhav Bajpai, Gagan Gandhi, Barnali Dey, Manaswini Pradhan, Ranjit Kumar, Sahu, Ebenezer Olaniyi, Khashman Obaloluwa, Adnan, Rau, Chien-Yeh Hsiao-Hsien, Yu-An Hsu, Suleman Lin, Anis Atique, Li-Ming Fuad, Ming-Huei Wei, Hsu, Sabah Abdulkareem, Hussien Anwer, Yousra Yossif Radhi, H Ahmed Fadil, Mahdi, Ziqiu Kang, Cagatay Catal, Bedir Tekinerdogan, Fahime Khozeimeh, Danial Sharifrazi, Navid Hoseini Izadi, Javad Hassannataj Joloudari, Afshin Shoeibi, Roohallah Alizadehsani, Juan Gorriz, Sudhansh Sharma, Bhavya Sharma
Abstract
Diabetes is a widespread illness for which there is now no treatment. Diabetes-related flaws cost our nation a lot to treat each year, as projected in the therapy, so it's crucial to predict the patients' conditions with greater precision. Accurate and reliable methodologies should be utilised to make predictions with a high level of accuracy and reliability. Utilizing neural networks and other artificial intelligence systems is one of these techniques. Given the accuracy of statistical models like the logistic regression model, a new combination of these statistical models and neural networks that has the least amount of error and the highest degree of dependability is examined in this study. The numerical results produced, When compared to the neural network and logistic regression approaches, acceptable results were obtained after the approach's accuracy and effectiveness were assessed on the basis of the aforementioned recommendation model, various experiences, and comparison. The performance standards used in this study for a hybrid neural network's use in neural network training to lower the error function are. The study on diabetes prediction using various supervised learning artificial neural network algorithms is presented in this publication. Data from 250 diabetic patients, ranging in age from 25 to 78, is used to train the network. Regression analysis is used to further examine how each method performs. To confirm an accurate forecast, the most effective algorithm's prediction accuracy is established.