• Ashish Sawant
• Prachi Awandekar
• Viraj Thak
• Dhawal Saratkar

Machine learning, powered by vast healthcare data, helps diagnose heart and liver diseases early, saving lives. This research investigates the use of hybrid machine learning approaches for accurate prediction of heart and liver diseases. We propose a novel model combining the strengths of Support Vector Machines (SVM), Logistic Regression (LR), and Decision Tree Classifier (DTC) to improve upon individual algorithms' limitations. The model leverages SVM's non-linearity handling, LR's interpretability, and DTC's feature extraction capabilities for robust disease prediction. This research contributes to the advancement of disease prediction with hybrid machine learning techniques. The proposed model offers a promising approach for improved diagnosis and patient care, while the individual component analysis provides valuable insights for future model development and refinement.

Machine Learning , Support Vector Machine , Logistic Regression , Decision Tree Classifier , Random Forest Classifier , Healthcare , heart and liver disease , prediction

"Heart & Liver Disease Prediction Using Hybrid Machine Learning Model ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.f576-f586, February-2024, Available :http://www.jetir.org/papers/JETIR2402572.pdf

"Heart & Liver Disease Prediction Using Hybrid Machine Learning Model ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppf576-f586, February-2024, Available at : http://www.jetir.org/papers/JETIR2402572.pdf