Revitalizing Disease Prediction: Modified Back propagation and Reformed feature extraction Approaches for Classification and Regression of Disease
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Abstract
Purpose: Diseases are unavoidable because of environmental factors, changes in diet, hereditary issues and many other factors; hence, it is important to detect diseases via various techniques in the healthcare sector to identify and diagnose the disease. Therefore, the proposed model focuses on employing advanced techniques for detecting heart disease, thyroid disease, and hepatitis, as these diseases have become common in recent years, along with the prediction of heart rate.
Materials and Methods: The proposed work employs modified PCA (principal component analysis) for dimensionality reduction to extract appropriate features for the model by utilizing two learning rates (LR1 and L2). Furthermore, the modified back propagation (BP) method is used for effective classification of heart, thyroid, hepatitis, and heart rate prediction by incorporating adaptive Gaussian white noise (AWGN). In the proposed model, three different datasets are utilized: a heart disease dataset, a thyroid dataset, a hepatitis dataset for classification, and a heart rate prediction dataset for regression.
Results: The accuracy, precision, recall, and F1 scores obtained by the proposed model for the heart disease dataset are 97.8%, 98%, 98%, and 98%, respectively. Similarly, 97.2%, 98%, 89%, and 93% for the thyroid dataset, respectively. Finally, the accuracy, precision, recall, and F1 score obtained by the proposed model for hepatitis are 95%, 98%, 88%, and 92%, respectively. Like the classification of diseases, heart rate prediction was also evaluated via different metrics, such as the RMSE, MSE, MAE, and R2. The MAE obtained by the proposed model for the heart rate prediction dataset is 0.112; likewise, the R2 obtained is 0.99, the MSE attained is 0.022, and the RMSE value obtained is 0.1488.
Conclusion: The results of the proposed mechanism reflect its ability to detect different diseases effectively. This is due to the successful implementation of advanced AI approaches in the proposed framework.
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33- Md Riajuliislam, Khandakar Zahidur Rahim, and Antara Mahmud, "Prediction Of Thyroid Disease (Hypothyroid) In Early Stage Using Feature Selection And Classification Techniques."
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35- Surjeet Dalal et al., "Enhancing thyroid disease prediction with improved XGBoost model and bias management techniques." Multimedia Tools and Applications, pp. 1-32, (2024).
36- Dinh‐Van Phan, Chien‐Lung Chan, Ai‐Hsien Adams Li, Ting‐Ying Chien, and Van‐Chuc %J International Journal of Cancer Nguyen, "Liver cancer prediction in a viral hepatitis cohort: A deep learning approach." Vol. 147 (No. 10), pp. 2871-78, (2020).
37- Elias Dritsas and Maria %J Computers Trigka, "Supervised machine learning models for liver disease risk prediction." Vol. 12 (No. 1), p. 19, (2023).
38- Pyla Jyothi, A LOKESH KUMAR, G KAVYA SRI, D DAKSHAYANI, and K KAVYA, "DISEASE PREDICTION USING NAIVE BAYES, RANDOM FOREST, DECISION TREE, KNN ALGORITHMS." I-Manager's Journal on Computer Science, Vol. 11 (No. 4), (2024).
39- [Online]. Available: https://archive.ics.uci.edu/dataset/45/heart+disease.
40- Ross Quinlan, "Thyroid disease data set." Thyroid Disease Data Set. https://archive. ics. uci. edu/ml/datasets/thyroid+ disease (accessed Jul. 03, 2022), (1987).
41- [Online]. Available: https://archive.ics.uci.edu/dataset/46/hepatitis.
42- Heart Rate Prediction Dataset. [Online]. Available: https://www.kaggle.com/datasets/saurav9786/heart-rate-prediction?select=time_domain_features_train.csv.
43- G Manikandan, B Pragadeesh, V Manojkumar, AL Karthikeyan, R Manikandan, and Amir H %J Informatics in Medicine Unlocked Gandomi, "Classification models combined with Boruta feature selection for heart disease prediction." Vol. 44p. 101442, (2024).
44- Ali Raza, Fatma Eid, Elisabeth Caro Montero, Irene Delgado Noya, Imran %J BMC Medical Informatics Ashraf, and Decision Making, "Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models." Vol. 24 (No. 1), p. 364, (2024).
45- George Obaido et al., "An interpretable machine learning approach for hepatitis b diagnosis." Vol. 12 (No. 21), p. 11127, (2022).
46- C Vijayalakshmi and S Pakkir %J Measurement: Sensors Mohideen, "Survival prediction model with lion optimization for Hepatitis-B patients using supervised and unsupervised learning algorithm." Vol. 32p. 100958, (2024).
2- Devansh Shah, Samir Patel, and Santosh Kumar %J SN Computer Science Bharti, "Heart disease prediction using machine learning techniques." Vol. 1pp. 1-6, (2020).
3- Rahul Katarya, Sunit Kumar %J Health Meena, and Technology, "Machine learning techniques for heart disease prediction: a comparative study and analysis." Vol. 11pp. 87-97, (2021).
4- Yassine Habchi et al., "Machine learning and vision transformers for thyroid carcinoma diagnosis: A review." arXiv preprint arXiv:2403.13843, (2024).
5- Yanan Che, Meng Zhao, Yan Gao, Zhibin Zhang, and Xiangyang Zhang, "Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases." Frontiers in Molecular Biosciences, Vol. 11p. 1483326, (2024).
6- Hafiz Abbad Ur Rehman, Chyi-Yeu Lin, Zohaib Mushtaq, Shun-Feng %J Arabian Journal for Science Su, and Engineering, "Performance analysis of machine learning algorithms for thyroid disease." pp. 1-13, (2021).
7- Tahir Alyas, Muhammad Hamid, Khalid Alissa, Tauqeer Faiz, Nadia Tabassum, and Aqeel %J BioMed Research International Ahmad, "Empirical method for thyroid disease classification using a machine learning approach." Vol. 2022(2022).
8- Yanhui Guo, Yi Feng, Fuli Qu, Li Zhang, Bingyu Yan, and Jingjing %J Plos one Lv, "Prediction of hepatitis E using machine learning models." Vol. 15 (No. 9), p. e0237750, (2020).
9- Michael Onyema Edeh et al., "Artificial intelligence-based ensemble learning model for prediction of hepatitis C disease." Vol. 10p. 892371, (2022).
10- Arsalan Khan, Moiz Qureshi, Muhammad Daniyal, Kassim %J Health Tawiah, and Social Care in the Community, "A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction." Vol. 2023(2023).
11- Nadikatla Chandrasekhar and Samineni %J Processes Peddakrishna, "Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization." Vol. 11 (No. 4), p. 1210, (2023).
12- Subhash Mondal, Ranjan Maity, Yachang Omo, Soumadip Ghosh, and Amitava Nag, "An Efficient Computational Risk Prediction Model of Heart Diseases Based on Dual-Stage Stacked Machine Learning Approaches." IEEE Access, (2024).
13- Farida Brahimi, Aicha Aid, Mourad Amad, Abdelghani Mehennaoui, and Abderahmane Baadache, "Enhanced K-Nearest Neighbors for Smart Cardiovascular Disease Prediction in IoT System." Revue d'Intelligence Artificielle, Vol. 38 (No. 4), (2024).
14- Sumit Sharma, Mahesh %J International Journal of Innovative Technology Parmar, and Exploring Engineering, "Heart diseases prediction using deep learning neural network model." Vol. 9 (No. 3), pp. 2244-48, (2020).
15- Hosam F El-Sofany, "Predicting heart diseases using machine learning and different data classification techniques." IEEE Access, (2024).
16- Awais Mehmood et al., "Prediction of heart disease using deep convolutional neural networks." Vol. 46 (No. 4), pp. 3409-22, (2021).
17- Muhammad Amir Khan et al., "Optimal feature selection for heart disease prediction using modified Artificial Bee colony (M-ABC) and K-nearest neighbors (KNN)." Scientific Reports, Vol. 14 (No. 1), p. 26241, (2024).
18- C Ahmed Telmoud, M Mohamed Saleck, and M Cheikh Tourad, "ADVANCING HEART DISEASE DIAGNOSIS AND ECG CLASSIFICATION USING MACHINE LEARNING." J Theor Appl Inf Technol, Vol. 102pp. 2608-23, (2024).
19- Syed Nawaz Pasha, Dadi Ramesh, Sallauddin Mohmmad, and A Harshavardhan, "Cardiovascular disease prediction using deep learning techniques."
20- Mohd Ashraf, MA Rizvi, Himanshu %J Asian Journal of Computer Science Sharma, and Technology, "Improved heart disease prediction using deep neural network." Vol. 8 (No. 2), pp. 49-54, (2019).
21- Dengqing Zhang et al., "Heart disease prediction based on the embedded feature selection method and deep neural network." Vol. 2021pp. 1-9, (2021).
22- Shambhu Bhardwaj, Vipul Vekariya, Baldev Singh, Sri Vinay, Alli Arul, and Maria Daya Roopa, "Improved healthcare monitoring of coronary heart disease patients in time-series fashion using deep learning model." Measurement: Sensors, Vol. 32p. 101053, (2024).
23- Misha Urooj Khan et al., "Artificial neural network-based cardiovascular disease prediction using spectral features." Vol. 101p. 108094, (2022).
24- Aniruddha Dutta, Tamal Batabyal, Meheli Basu, and Scott T %J Expert Systems with Applications Acton, "An efficient convolutional neural network for coronary heart disease prediction." Vol. 159p. 113408, (2020).
25- Mert Ozcan and Serhat %J Healthcare Analytics Peker, "A classification and regression tree algorithm for heart disease modeling and prediction." Vol. 3p. 100130, (2023).
26- Wasif Akbar et al., "Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases." Vol. 75 (No. 3), (2023).
27- C Uma and P Rathiga, "An Optimized Deep Ensemble Super-Learner Model For Thyroid Disease Classification." Library of Progress-Library Science, Information Technology & Computer, Vol. 44 (No. 3), (2024).
28- Dhyan Chandra Yadav, Saurabh %J Indian Journal of Public Health Research Pal, and Development, "Discovery of hidden pattern in thyroid disease by machine learning algorithms." Vol. 11 (No. 1), pp. 61-66, (2020).
29- George Obaido et al., "An improved framework for detecting thyroid disease using filter-based feature selection and stacking ensemble." IEEE Access, (2024).
30- Md Asfi-Ar-Raihan Asif et al., "Computer Aided Diagnosis of Thyroid Disease Using Machine Learning Algorithms."
31- Khandaker Mohammad Mohi Uddin, Abdullah Al Mamun, Anamika Chakrabarti, and Rafid Mostafiz, "An ensemble machine learning-based approach to predict thyroid disease using hybrid feature selection." Biomedical Analysis, Vol. 1 (No. 3), pp. 229-39, (2024).
32- Mohemmed Sha, "Quantum intelligence in medicine: Empowering thyroid disease prediction through advanced machine learning." IET Quantum Communication, Vol. 5 (No. 2), pp. 123-39, (2024).
33- Md Riajuliislam, Khandakar Zahidur Rahim, and Antara Mahmud, "Prediction Of Thyroid Disease (Hypothyroid) In Early Stage Using Feature Selection And Classification Techniques."
34- Yasir Iqbal Mir, Sonu %J International Journal of Scientific Mittal, and Technology Research, "Thyroid disease prediction using hybrid machine learning techniques: An effective framework." Vol. 9 (No. 2), pp. 2868-74, (2020).
35- Surjeet Dalal et al., "Enhancing thyroid disease prediction with improved XGBoost model and bias management techniques." Multimedia Tools and Applications, pp. 1-32, (2024).
36- Dinh‐Van Phan, Chien‐Lung Chan, Ai‐Hsien Adams Li, Ting‐Ying Chien, and Van‐Chuc %J International Journal of Cancer Nguyen, "Liver cancer prediction in a viral hepatitis cohort: A deep learning approach." Vol. 147 (No. 10), pp. 2871-78, (2020).
37- Elias Dritsas and Maria %J Computers Trigka, "Supervised machine learning models for liver disease risk prediction." Vol. 12 (No. 1), p. 19, (2023).
38- Pyla Jyothi, A LOKESH KUMAR, G KAVYA SRI, D DAKSHAYANI, and K KAVYA, "DISEASE PREDICTION USING NAIVE BAYES, RANDOM FOREST, DECISION TREE, KNN ALGORITHMS." I-Manager's Journal on Computer Science, Vol. 11 (No. 4), (2024).
39- [Online]. Available: https://archive.ics.uci.edu/dataset/45/heart+disease.
40- Ross Quinlan, "Thyroid disease data set." Thyroid Disease Data Set. https://archive. ics. uci. edu/ml/datasets/thyroid+ disease (accessed Jul. 03, 2022), (1987).
41- [Online]. Available: https://archive.ics.uci.edu/dataset/46/hepatitis.
42- Heart Rate Prediction Dataset. [Online]. Available: https://www.kaggle.com/datasets/saurav9786/heart-rate-prediction?select=time_domain_features_train.csv.
43- G Manikandan, B Pragadeesh, V Manojkumar, AL Karthikeyan, R Manikandan, and Amir H %J Informatics in Medicine Unlocked Gandomi, "Classification models combined with Boruta feature selection for heart disease prediction." Vol. 44p. 101442, (2024).
44- Ali Raza, Fatma Eid, Elisabeth Caro Montero, Irene Delgado Noya, Imran %J BMC Medical Informatics Ashraf, and Decision Making, "Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models." Vol. 24 (No. 1), p. 364, (2024).
45- George Obaido et al., "An interpretable machine learning approach for hepatitis b diagnosis." Vol. 12 (No. 21), p. 11127, (2022).
46- C Vijayalakshmi and S Pakkir %J Measurement: Sensors Mohideen, "Survival prediction model with lion optimization for Hepatitis-B patients using supervised and unsupervised learning algorithm." Vol. 32p. 100958, (2024).
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| Issue | Articles in Press | |
| Section | Original Article(s) | |
| Keywords | ||
| Keywords- Disease Prediction Principal Component Analysis Back Propagation Adaptive Gaussian White Noise Heart Disease Thyroid Hepatitis | ||
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How to Cite
1.
G JC, Subhajini A. Revitalizing Disease Prediction: Modified Back propagation and Reformed feature extraction Approaches for Classification and Regression of Disease. Frontiers Biomed Technol. 2026;.
