IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 13, No. 4, December 2024, pp. 3739~3749 ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i4.pp3739-3749
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A review of machine learning methods to build predictive models for male reproductive health Ariawan Adimoelja1, Wayan Firdaus Mahmudy2, Diva Kurnianingtyas2 1
Doctoral Program in Medical Science, Faculty of Medicine, Universitas Brawijaya, Malang, Indonesia 2 Department of Informatics Engineering, Faculty of Computers Science, Universitas Brawijaya, Malang, Indonesia
Article Info
ABSTRACT
Article history:
Developing of artificial intelligence (AI) technology in the medical sector, especially in the part of male reproduction and infertility, is growing rapidly. In both supervised learning and unsupervised learning, AI has been tested and applied to medical personnel to treat their patients. Calculations from simple to complex probability and a combination of some different methods have conducted results of accurate and precise. The results can help determine the condition of male infertility. Artificial neural network (ANN) and fuzzy inference system (FIS) are AI techniques applied to male health issues. ANN is adequate for processing large amounts of combined data in a short time. ANN also has a high level of accuracy and excellent adaptive capabilities. Afterwards, FIS can reflect problems using models with easy to understand, flexible, and also competent to model complex linear functions for decision-making. Based on the advantages of ANN and FIS, it is hoped acquiring prediction results of better and more accurate in male health issues.
Received Dec 19, 2023 Revised Mar 10, 2024 Accepted Mar 21, 2024 Keywords: Artificial intelligence Artificial neural network Fuzzy inference system Male health Medical
This is an open access article under the CC BY-SA license.
Corresponding Author: Wayan Firdaus Mahmudy Department of Informatics Engineering, Faculty of Computer Science, Brawijaya University Jl. Veteran No.10-11, Ketawanggede, Lowokwaru, Malang, Indonesia 65145 Email: wayanfm@ub.ac.id
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INTRODUCTION Difficulty in conceiving offspring in couples of childbearing age is still a problem. Although the development of therapy in the medical field to overcome the infertility issue has been very developed. In the last half-century, the resolution of infertility problems has been more emphasized in the process of handling therapy in the reproductive system of both men and women [1]. The therapy stage not only takes a lot of time but also costs and sometimes does not produce results. The problem becomes an impractical and difficult obstacle for young couples. For young couples experiencing fertility issues, the treating procedure refers to the many stages of examination. As a result, many are reluctant to have their fertility checked because it is very impractical. The success of fertility treatment is not only determined by the number of examination procedures but also by the doctor's expertise in exploring the patient's medical record. Time constraints are a contributing factor to the failure of treatment because there are often many records of medical and family that have not been clarified. Based on this explanation, an artificial intelligence (AI) approach can make early predictions carefully on fertility issues [2], [3]. AI can be applied as a means of supporting clinical guidance to improve the accuracy of diagnosis and reduce the possibility of misdiagnosis. AI can be utilized as a repository of necessary medical data. By utilizing AI, it can find relationships between the necessary data to produce a significant prediction solution [4].
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