International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 16, No. 4, 8 Aug. 2024
Cover page and Table of Contents: PDF (size: 1625KB)
A Machine Learning Based Intelligent Diabetic and Hypertensive Patient Prediction Scheme and A Mobile Application for Patients Assistance
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Author(s)
Index Terms
Diabetic and Hypertension, Disease Prediction, Machine Learning, Time and Cost Burden, Mobile Application, Treatment Suggestion, Doctor Search, Medicine Suggestion
Abstract
The inaccurate detection of diabetes and hypertension causes’ time wastage and a cost burden due to higher amounts of medicine intake and health problems. The previous works did not investigate machine learning (ML)-based diabetic and hypertension patient prediction by using multiple characteristics. This paper utilizes ML algorithms to predict the presence of diabetes and hypertension in patients. By analyzing patient data, including medical records, symptoms, and risk factors, the proposed system can provide accurate predictions for early detection and intervention. This paper makes a list of eighteen characteristics that can be used for data set preparation. With a classification accuracy of 93%, the Support Vector Machine is the best ML model in our work and is used for the diabetic and hypertension disease prediction models. This paper also gives a new mobile application that alleviates the time and cost burden by detecting diabetic and hypertensive patients, doctors, and medical information. The user evaluation and rating analysis results showed that more than sixty five percent of users declared the necessity of the proposed application features.
Cite This Paper
Md. Amdad Hossain, Mahfuzulhoq Chowdhury, "A Machine Learning Based Intelligent Diabetic and Hypertensive Patient Prediction Scheme and A Mobile Application for Patients Assistance", International Journal of Information Technology and Computer Science(IJITCS), Vol.16, No.4, pp.29-44, 2024. DOI:10.5815/ijitcs.2024.04.02
Reference
[1]M. Nour et al., “Automatic classification of hypertension types based on personal features by machine learning algorithms,” Mathematical Problems in Engineering, vol. 2020, no. xx, pp. 1-14, 2020.
[2]F. Haque et al., “Performance analysis of conventional machine learning algorithms for diabetic sensorimotor polyneuropathy severity classification using nerve conduction studies,” Computational Intelligence and Neuroscience, vol. 11, no. 5, pp. 1-10, 2021.
[3]M. Bader Alazzam et al., “Identification of diabetic retinopathy through machine learning,” Mobile Information Systems, vol. 2021, no. xx, pp. 1-8, 2021.
[4]E. Adane et al., “The cost of illness of hypertension and associated factors at the university of gondar comprehensive specialized hospital northwest ethiopia”, ClinicoEconomics and Outcomes Research, vol. 12, no. xx, pp. 133–140, 2020.
[5]L. Zhang et al., “Adaboost: Still a powerful machine learning tool,” Journal of Zhejiang University SCIENCE C,vol. 13, no. 5, pp. 349–367, 2012.
[6]M. S. Rahman et al., “Awareness, treatment, and control of diabetes in bangladesh: A nationwide population-based study,” PloS one, vol. 10, no. 2, pp. 1–14, 2015.
[7]M. S. Islam et al., “Machine learning based decision support system for diabetes management,” in Diabetes and Metabolic Syndrome: Clinical Research and Reviews, vol. 13, no. 1, pp. 123–130, 2019.
[8]WHO, “National guidelines for management of hypertension in bangladesh,” https://apps.who.int/iris/bitstream/handle/10665/ 279486/9789843368553-eng.pdf ?sequence=1, last accessed on july 2023.
[9]M. J. Husain et al., “Cost of primary care approaches for hypertension management and risk based cardiovascular disease prevention in bangladesh: A hearts costing tool application,” BMJ open, vol. 12, no. xx, pp. 1-19, 2022.
[10]M. Maniruzzaman et al., “Classification and prediction of diabetes disease using machine learning paradigm,” Health information science and systems, vol. 8, no. xx, pp. 1–14, 2020.
[11]L. Jia et al., “Pe-dim: An efficient probabilistic ensemble classification algorithm for diabetes handling class imbalance missing values,” in IEEE Access, vol. 10, pp. 107459–107476, 2022.
[12]D. LaFreniere et al., “Using machine learning to predict hypertension from a clinical dataset,” in 2016 IEEE symposium series on computational intelligence (SSCI), pp. 1–7, 2016.
[13]G. Guo et al., “Knn model-based approach in classification,” OTM Confederated International Conferences, Lecture Notes in Computer Science, vol 2888, Springer, Berlin, Heidelberg, pp 986–996, 2003.
[14]M. A. Makroum et al., “Machine learning and smart devices for diabetes management: Systematic review”, in Sensors, vol. 22, no. 5, pp. xx-xx, 2022.
[15]V. Mohan et al., “The rising burden of diabetes and hypertension in southeast asian and african regions: Need for effective strategies for prevention and control in primary health care settings,” International journal of hypertension, vol. 2013, pp. 1-10, 2013.
[16]S. I. Ayon et al., “Diabetes prediction: A deep learning approach,” International Journal of Information Engineering Electronic Business, vol. 11, no. 2, pp. 1–12, 2019.
[17]Android, “https://www.android.com/intl/en-ca/,” last accessed on july 2023.
[18]C. Khawas et al., “Application of firebase in android app developmenta study,” International Journal of Computer Applications, vol. 179, no. 46, pp. 49–53, 2018.
[19]Tiangolo, Fastapi, “https://fastapi.tiangolo.com/,” last accessed on july 2023.
[20]T. Lv et al., “Survey on json data modelling,” Journal of Physics: Conference Series, IOP Publishing, vol. 1069, no. xx, pp. 1-5, 2018.
[21]Scikit-Learn Contributors, “Scikit Learn - randomforestclassifier,” https://scikit learn.org/stable/modules/generated/ sklearn.ensemble.RandomForestClassifier.html, last accessed on july 2023.
[22]C. M. Bishop et al., “Pattern Recognition and Machine Learning,” springer, pp. 1-100, last accessed on january 2023.
[23]Scikit-Learn Contributors, “Scikit Learn – Decision Tree Classifier,” https://scikit learn.org/stable/modules/generated/ sklearn.tree.DecisionTreeClassifier.html, last accessed on july 2022.
[24]I. Tasin et al., “Diabetes prediction using machine learning and explainable AI techniques,” Healthcare Technology Letters, vol. 10, pp. 1–10, 2023.
[25]M. M. Islam et al., “Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia,” PLoS ONE, vol. 18, no. 8, pp. 1–20, article id e0289613, 2023.