Diabetes diagnosis system using modified Naive Bayes classifier

Abstract

<span>In today’s world, Diabetes is one of these diseases and is now a big growing health problem. The techniques of data mining have been widely applied to extract knowledge from medical databases. In this work, a Medical Diagnosis system of Diabetes is proposed for the ‎diagnosis of diabetes in a manner ‎that is rapid and cost-effective. three stages are ‎involved in the proposed diabetes diagnosis system (DDS) including: dataset constructing, preprocessing and classification algorithm using traditional Naïve Bayesian ‎‎(TNB) and modified Naïve Bayesian (MNB)). MNB Classifier is a modified NB that is used to ‎enhance the accuracy of ‎diagnosis, by adding a proposed modest model to help separate ‎the overlapping diagnosis classes. The outcome‎ ‎showed that the accuracy of MNB classifier is generally higher than that of ‎TNB ‎classifier for all sets of features. An accuracy of about (63%) was achieved for the TNB ‎model, whereas ‎that of the MNB model is (100%). The experimental results showed that ‎the MNB is better than the traditional ‎NB in both two cases of constructed medical ‎datasets; the first case of filling the missing values by experiences and ‎the second case of filling ‎missing values by K-nearest neighbor (KNN) algorithm.</span>

Data Mining Selection of Prospective Government Employees with Employment Agreements using Naive Bayes Classifier

“…This algorithm can predict the future based on past historical data. The Naïve Bayes method does not require extensive data in the classification process but a high level of accuracy so that data can be processed quickly (Afdhaluzzikri et al, 2022;Alwan et al, 2022). The researcher cites the Thomas Bayes formula (Gupitha, 2018;Prakoso et al, 2019), while the general formula is P (H|X) = P (X|H) P (H) …….…”

Implementation of Deep CNN Model for the Detection of Plant Leaf Disease

“…Neural networks are used in Deep Learning to understand representations of features directly from the data. Neural networks, which are modeled after biological nerve systems, incorporate several nonlinear processing layers utilizing straightforward components that operate in paralle [19]l. Deep learning models can classify objects with state-of-the-art accuracy, sometimes outperforming human ability.…”

“…The Pooling layers are placed after the initial convolutional layer, and the fully connected layer is lastly placed. With each layer, the Convolutional Neural Network's complexity gradually rises, allowing it to recognise greater portions of the image [23]. In the first layers, fundamental components like colours and borders are highlighted Plants typically develop diseases when their normal structure, growth, function, or other activities are repeatedly disrupted by some causal agent, leading to an abnormal physiological process.…”