Revolutionizing Medical Diagnosis with Convolutional Neural Network: A Data-Driven Solution to Improve Accuracy in Disease Detection

Mohammad Rifaq; Daud Khan

Author(s)

Mohammad Rifaq Department of Computer Science , Iqra National University, Peshawar 25100, Pakistan

Daud Khan Department of Computer Science , Iqra National University, Peshawar 25100, Pakistan

Abstract

Currently, the global mortality rate is on the rise due to the increasing prevalence of various diseases. This surge in deaths is largely attributed to the growing number of patients suffering from major health conditions. Unfortunately, many patients are often misled by medical practitioners, and in some cases, these physicians lack the necessary expertise to accurately diagnose specific diseases. This presents a significant challenge in healthcare today. Timely and accurate diagnosis of diseases remains a critical issue due to a shortage of specialists and a lack of experience in managing similar cases. Traditional manual diagnostic systems for diseases are often unreliable due to uncertainties in clinical data and medical knowledge. In this research, a deep learning algorithm is developed, specifically a Convolutional Neural Network (CNN), to detect diseases. A datasets from the UCI Machine Learning Repository is utilized, applying machine learning techniques to diagnose these conditions without requiring the assistance of medical professionals. Additionally, the proposed model can predict whether an individual is at risk of developing a particular disease in the future based on key factors associated with the disease. The medical datasets used in the proposed study include Breast Cancer, Diabetes, Heart Disease, and Liver Disorders from UCI. The data underwent preprocessing before being input into the CNN. The accuracy of the proposed model is compared with previous research, demonstrating its effectiveness and superior training.

Publication Details

Page(s) 10-25

DOI DOI not available

Published Journal: The Sciencetech, Volume: 6, Issue: 1, Year: 2025

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Keywords

Convolutional Neural Network, deep learning, Disease diagnosis, Medical Diseases

References

Kawarkhe M.,Kaur P. .2024 .Prediction of Diabetes Using Diverse Ensemble Learning Classifiers. Procedia Computer Science, 235 : 403-413.

Khan H.,Bilal A.,Aslam M. A.,Mustafa H. .2024 .Heart Disease Detection: A Comprehensive Analysis of Machine Learning, Ensemble Learning, and Deep Learning Algorithms. , : .

Nabeel M.,Majeed S.,Awan M. J.,Din H.,Wasique M.,Nasir R. .2021 .Review on Effective Disease Prediction through Data Mining Techniques. International Journal on Electrical Engineering & Informatics, 13(3) : .

Nassif A. B.,Mahdi O.,Nasir Q.,Talib M. A.,Azzeh M. .2018 .Machine learning classifications of coronary artery disease. In 2018 International Joint Symposium on artificial intelligence and natural language processing (iSAI-NLP), : 1-6.

Patil M.,Mathur H. .2020 .Study of Machine Learning Algorithms for Prediction and Diagnosis of Cardiovascular Diseases: A Review.. , : .

Ramana B. V.,Boddu R. S. K. .2019 .Performance comparison of classification algorithms on medical datasets. In 2019 IEEE 9th Annual computing and communication workshop and conference (CCWC), : 0140-0145.

Ramzan M. .2021 .Comparing and evaluating the performance of WEKA classifiers on critical diseases. In 2021 1st India International Conference on Information Processing (IICIP), : 1-4.

Rathinaeaswari S. P.,Santhi V. .2023 .A New Efficient and PrivacyPreserving Hybrid Classification Model for Patient-Centric Clinical Decision Support System. Journal of Advanced Research in Applied Sciences and Engineering Technology, 33(1) : 299-316.

Reddy S. S.,Sethi N.,Rajender R. .2020 .Diabetes correlated renal fault prediction through deep learning. EAI Endorsed Transactions on Pervasive Health and Technology, 6(24) : e4-e4.

Sabariah M. M. K.,Hanifa S. A.,Sa M. S. .2022 .Early detection of type II Diabetes Mellitus with random forest and classification and regression tree (CART). (ICAICTA), : 238-242.

Shekhar Sai,M. Sai,Chand Mani,Mary Gladence Y.,L. Y. .2020 .Heart Disease Prediction Using Machine Learning. In International Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, : 603-609.

Sinwar D .2020 .Artificial intelligence technology in healthcare: security and privacy issues/edited by. parameters, (4) : 5.

Sujith J.,Kumar P. K.,Reddy S. J. M.,Kanhe A. .2023 .Computative analysis of various techniques for classification of liver disease. In Journal of Physics: Conference Series, 1 : 012035.

Thangavel S.,Selvaraj S.,Keerthika K. .2024 .Analyzing Machine Learning Classifiers for the Diagnosis of Heart Disease. EAI Endorsed Transactions on Pervasive Health and Technology, 10 : .

Thomas J.,Princy R. T. .2022 .Human heart disease prediction system using data mining techniques. In 2022 international conference on circuit, power and computing technologies (ICCPCT), : 1-5.

Wang Y.,Yu Z.,Jiang Y.,Liu Y.,Chen L.,Liu Y. .2021 .A framework and its empirical study of automatic diagnosis of traditional Chinese medicine utilizing raw free-text clinical records. Journal of Biomedical Informatics, 45(2) : 210-223.

Wee B. F.,Sivakumar S.,Lim K. H.,Wong W. K.,Juwono F. H. .2024 .Diabetes detection based on machine learning and deep learning approaches. Multimedia Tools and Applications, 83(8) : 24153-24185.