Heart failure and cardiovascular diseases are two severe medical problem which needs immediate preventive mea sures and early recognition of risk factors. Cardiovascular Car diovascular cardiovascular cardiovascular cardiovascular cardio vascular risk. But, many of the existing prediction systems focus more on developing a risk stratification than providing patient specific recommendations tailored to their clinical conditions. In this paper, we propose an Enhanced Machine Learning System for heart failure as well as Personalised Health Recom mendation Engine. To discover an effective prediction model, the proposed system collects demographic, clinical and lifestyle related data, preprocess it, select the features and evaluates various machine learning algorithms. It includes a risk category, prediction probability, confidence data along with Heart Health Score in the output of the prediction. The main contribution of the proposed system is the Person alised Recommendation Engine which takes machine learning predictions and circadian rhythm parameters (blood pressure, cholesterol, BMI, diabetes status, smoking habits) with phys ical activity, sleep duration stress level and dietary habits as individual parameters. Instead of issuing the same prevention recommendations to every user in a specific risk group, it produces individualized preventive recommendations based on each user’s health profile. Explainable Artificial Intelligence methods can be adopted to ascertain the factors that impact the prediction. All these services are brought together under one interac tive web-based system through the proposed framework. It is interactive, user-centric, user-centric, user-centric, user-centric, user-centric, and user-centric.
Keywords
Heart Failure Prediction Machine Learning Per V. Shiva Pranith Sai Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning) Parul Institute of Engineering and Technology Parul University, Vadodara, Gujarat, India Email: 2303031460176@paruluniversity.ac.in K. B. N. V. S. G. Dhanush Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning) Parul Institute of Engineering and Technology Parul University, Vadodara, Gujarat, India Email: 2303031460085@paruluniversity.ac.in Jyothi Chowdary Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University, Vadodara, Gujarat, India Under the Guidance of Assistant Professor sonalised Recommendation Engine Heart Health Score Explain able AI Feature Selection XGBoost Random Forest Healthcare Analytics
This study introduces a Better Machine Learning System for Guessing if Someone Might Have Heart Failure and Giving Personal Health Tips. The system combines the ability to predict health risks using machine learning with advice that is aimed at helping people stay healthy. Many different machine learning methods are tested to find the one for making predictions. The system also uses ways to pick the important information and adjust settings to make the predictions more accurate and the computer work faster. The results from the system include things like risk groups, chance of getting sick proof of the prediction and a score, for heart health. The main contribution of the suggested system is the Cus tomized Recommendation Engine. Unlike systems that only place users into risk categories this system looks at personal clinical details and lifestyle choices to give recommendations that fit each individual. The use of AI makes the system more transparent by showing which factors affect the prediction results. Additional features such as prediction history, guidance for talking with doctors support, for finding hospital locations and health report generation make the system easier to use in life. In short the suggested framework goes beyond a disease prediction model. It turns into a personalized heart health awareness and support system. This system is designed to help people make decisions about their health. The system focuses on heart health. The system is meant to help people make decisions. The system is all about heart health. The system is designed to help people make decisions, about their health. The system is personalized. The system is detailed. The system is focused on heart health.
References
[1] S. Nikhar and A. M. Karandikar, “Prediction of heart disease using machine learning algorithms,” International Journal of Advanced Engi neering, Management and Science, vol. 2, no. 6, 2016. [2] N. Chandrasekhar and S. Peddakrishna, “Enhancing heart disease pre diction accuracy through machine learning techniques and optimization,” Processes, vol. 11, no. 4, p. 1210, 2023. [3] M. Ahmed, M. H. Sulaiman, M. M. Hassan, and T. Bhuiyan, “Predicting the classification of heart failure patients using optimized machine learning algorithms,” IEEE Access, 2025. [4] A. M. Qadri, A. Raza, K. Munir, and M. S. Almutairi, “Effective feature engineering technique for heart disease prediction with machine learning,” IEEE Access, vol. 11, pp. 56214–56224, 2023. [5] Additional research papers related to machine learning, explainable artificial intelligence, heart disease prediction, feature selection, and personalized healthcare recommendations can be added based on the final literature review
📋 How to Cite This Paper
Chekuri Manmohan, V. Shiva Pranith Sai, K. Hari Sai Venkat, K. B. N. V. S. G. Dhanush, K. Jaya Sankar, Jyothi Chowdary (2026). Optimized Machine Learning System for Heart Failure Prediction. International Journal of Computer Science Engineering Techniques, 10(5), 339–347. ISSN: 2455-135X. DOI: https://doi.org/10.5281/zenodo.23421912
