• Harshwardhan Natu
• Darshan Deshpande
• Shardul Dewalkar

This paper outlines the development of a healthcare application that utilizes machine learning (ML) techniques to enhance patient outcomes. The app assists healthcare professionals in making informed decisions by analyzing vast amounts of patient data. The ML algorithms are trained on various healthcare data sources, including electronic health records, clinical notes, and medical images. The app's predictive functionality enables healthcare professionals to recognize patients at high risk of developing various health conditions and to take proactive measures to mitigate these risks. The app's interface is designed to be user-friendly and intuitive, allowing healthcare professionals to quickly access and analyze patient data. The ML models used in the application are continually updated with new data, ensuring that the app remains current and effective. A clinical trial was conducted with a group of healthcare professionals to assess the app's effectiveness. The results demonstrate that the app enhances patient outcomes by providing more precise diagnoses, reducing hospital readmissions, and improving treatment efficacy. This research shows the potential of ML-based healthcare applications to improve patient outcomes by providing healthcare professionals with accurate and timely information. As healthcare providers face increasing demands to deliver high-quality care while managing costs, ML-based healthcare applications present a promising solution to these challenges.

healthcare, patient, data, costs, ML algorithm’s.

"Machine learning driven disease prediction system", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.l376-l380, May-2023, Available :http://www.jetir.org/papers/JETIR2305B56.pdf

"Machine learning driven disease prediction system", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppl376-l380, May-2023, Available at : http://www.jetir.org/papers/JETIR2305B56.pdf