empowering heart disease prediction - integrating ai and advance techniques
Lokeshwar K,Jyotheswar T,V Gnana Prakash
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 19 April,2025 Pages:2007-2012
Abstract
Predicting heart disease remains a formidable challenge in the medical realm, demanding substantial time and expertise from healthcare professionals. This study introduces a novel approach to cardiac disease prediction utilizing a combination of traditional Machine Learning algorithms like LR, KNN, SVM, and LightGBM, alongside a groundbreaking Bi-Long ShortTerm Memory (Bi-LSTM) network. The proposed BiLSTM algorithm is evaluated using a 5-fold crossvalidation technique for rigorous validation and also survey report will be displayed. In this project, diverse datasets including Cleveland from UCI Kaggle are harnessed to assess the performance of the models. Comparative evaluations with prior studies in heart disease prediction underscore the efficacy of the proposed technique. Intriguingly, this research not only enriches the existing literature in heart disease prediction but also introduces a paradigm shift by incorporating the Bi-LSTM algorithm. The findings illuminate the Bi-LSTM process in discerning intricate patterns within cardiac data, bolstering the accuracy of predictions. Ultimately, this work paves the way for an innovative model creation technique, poised to revolutionize problem-solving in real-world scenarios with the display of survey report analysis.
Kewords
EMPOWERING HEART DISEASE, INTEGRATING AI, ADVANCED TECHNIQUES
Reference
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