#ComputerScience, #Python, #MATLAB, #AI, #AIML, #ML, #Research, #Medical, #HearDisease.
Inviting serious researchers into heart disease prediction before it reaches its peak level.
It has been found in various research papers that patients reach the hospital when the disease has reached its peak level; consulting doctors and experts before disease maturity is an advantage.
There are various technologies that can be discussed, including basic training, ideas, and problem-solving topics.
Hello Mr. Jafri. Kindly connect @ [email protected] to discuss about the same.
Dear Mehdi Jafri ,
According to this study, the Random Forest technique has produced the best results, with an accuracy rate of 90% compared to other machine learning methods. The use of machine-learning methods, such as logistic regression and K-NN, to predict and categorize patients with heart disease is recommended by Jindal et al.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10258084/#:~:text=According%20to%20this%20study%2C%20the,recommended%20by%20Jindal%20et%20al.
Regards,
Shafagat
AI has been used to predict heart disease, enhancing early diagnosis and improving patient outcomes. Machine learning algorithms analyze medical data to predict heart disease risk. Common approaches include supervised learning models, deep learning, ensemble methods, feature selection and dimensionality reduction, and data fusion and integration. These methods help healthcare professionals identify individuals at high risk and intervene early with preventive measures or targeted treatments. However, rigorous validation and addressing challenges related to data privacy, interpretability, and bias mitigation are crucial for fostering trust and acceptance of AI-driven solutions in healthcare.
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