Artificial intelligence algorithms have an important and effective role in the medical field, especially in the field of diagnosing diseases. This research focuses on predicting the diagnosis of gestational diabetes by using the Iterative Dichotomiser3 (ID3) classifier algorithm, which is utilized to identify gestational diabetes; it was one of the most significant algorithms employed in this study. Training and testing are two critical phases of the research study. This study employed the Pima Indians Diabetes dataset, which comprised 768 women aged 21 and abovewith the eight reported traits. A feature selection stage, a discretization step, and using the classifier model for producing decision rules are all part of the Pima Indians diabetes data gathering process (Diabetes Dataset). In this study, the decision tree is employed to develop the classifier model, which is based on Diabetes training. The Iterative Dichotomiser3 (ID3) technique may be used to run the decision tree classification process. Diabetes is tested using decision rules, and the classifier implementation confusion matrix was retrieved from the testing portion. The system delivered high-quality results with a 94 percent accuracy rate.

The proposed model for getting the score matching of the deoxyribonucleic acid (DNA) sequence is introduced; the Neuro-Fuzzy procedure is the strategy actualized in this paper; it is used the collection of biological information of the DNA sequence performing with global and local calculations so as to advance the ideal arrangement; we utilize the pairwise DNA sequence alignment to gauge the score of the likeness, which depend on information gathering from the pairwise DNA series to be embedded into the implicit framework; an adaptive neuro-fuzzy inference system model is reasonable for foreseeing the matching score through the preparation and testing in neural system and the induction fuzzy system in fuzzy logic that accomplishes the outcome in elite execution.

This paper presented the issues of true representation and a reliable measure for analyzing the DNA base calling is provided. The method implemented dealt with the data set quality in analyzing DNA sequencing, it is investigating solution of the problem of using Neurofuzzy techniques for predicting the confidence value for each base in DNA base calling regarding collecting the data for each base in DNA, and the simulation model of designing the ANFIS contains three subsystems and main system; obtain the three features from the subsystems and in the main system and use the three features to predict the confidence value for each base. This is achieving effective results with high performance in employment.

The paper proposes an efficient approach applied in DNA base calling, which concerns efficiency and sensitivity. We utilized the Neuro-Fuzzy model in the analysis issues to determine the confidence value prediction in DNA base calling, that is solved by several attempts applied in the MATLAB tool, the model is implemented for the collected data for each base in the DNA sequencing. The model is designed by using the ANFIS tool, which contains  three subsystems a main system. We obtain three features (peakness, height, and spacing) for each base from the three subsystems and in the main system use these three features as the input to predict the confidence value for each base in the DNA. This achieves a high accuracy in the obtained results with high-performance.