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International Research Journal of Engineering and Technology (IRJET) Volume: 10 Issue: 04 | Apr 2023

www.irjet.net

e-ISSN: 2395-0056 p-ISSN: 2395-0072

CHOOSELYF Parth Adokar

Department of Information Technology Engineering Atharva College of Engineering Mumbai, India

Shubham Awasthi

Department of Information Technology Engineering Atharva College of Engineering Mumbai, India

Navkar Kumath

Atharva Dhopadkar

Department of Information Technology Engineering Atharva College of Engineering Mumbai, India

Prof. Nilemma Pathak

Department Of Information Technology Engineering. Atharva College of Engineering Mumbai, India

Department of Information Technology Engineering, Atharva College Of Engineering Mumbai. India

--------------------------------------------------------------------***------------------------------------------------------------------------extent of our understanding, this is the first study in a Abstract -- ChooseLyf is a phenomenon that offers

an application for smartphones to ease healthcare assistance via offering consumers with efficient and effective answers. Because the utilization of mobile phones has increased significantly, the model will provide users with accessibility to all services at any moment and from any location. In this contemporary and digital age, it is critical to handle problems in a computerized swifter manner, so the requirement during a crisis must be met in a user-friendly manner. This application includes a symptom tester, which analyzes the inputted patient's symptoms and recommends medicines or physicians based on the signs and symptoms. It is difficult to recall every detail in today's world, which is why the medication reminder maintains the individual using it updated on the prescribed dose without forgetting any. The accuracy of information is of the utmost importance due to the various kinds of user accounts, which aid in safeguarding information by only granting access to information to which the user is allowed. This application also includes a conversation area where the user can ask a Chatbot about their symptoms. A machine learning program that detects three main diseases, namely heart disease, diabetes, and Parkinson's disease, has also been developed.

structured literature review style for organizing and reviewing health care applications for cellphones. The meaning of "good health" can be viewed from a number of perspectives. Recognizing how different people view health on an individual level could provide specialists with useful information about what affects behavior with regard to of happiness and mental health in the general population.

Keywords — Diabetes, Heart, Parkinson, Regression, Machine Learning, Android.

Machine learning algorithms provide an efficient and effective method for predicting diseases based on a variety of criteria such as demographic data, medical background, lifestyle routines, and genetic variables. These algorithms can analyze large quantities of data and find trends that human specialists could have overlooked.

We all know that wellness is prosperity. Health has evolved into a crucial component for everyone on the globe. An individual's well-being is always an important factor in his or her capacity to thrive in life. Most people had undoubtedly overlooked this aspect since their daily lives are filled with busy schedules. In recent years, the use of machine learning methods for illness prediction has grown in prominence in healthcare. Diabetes, heart disease, and Parkinson's disease are just a few of the most prevalent long-term conditions that impact millions of people around the globe. Early identification and precise prediction of these illnesses can enhance patients' quality of life and allow healthcare workers to offer timely treatment and prevention measures.

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I. INTRODUCTION Mobile devices have become increasingly popular among both healthcare professionals and the broader population in the past few decades. The cell phone is a novel technology that integrates mobile contact and processing in a portable device, allowing for computing capabilities at the level of treatment. The primary goal of this research is to identify and describe smartphone-based medical equipment in the available literature in relation to their functionality. In this respect, we offer an organized examination of the available literature. To the greatest

Impact Factor value: 8.226

To address such circumstances, we are creating an Android program called "ChooseLyf" that includes a catalogue of diseases and their associated symptoms. Other tools include a BMI estimator, a medication reminder, and health tips. The user can look for local hospitals and clinics, as well as a machine learning system that uses linear regression analysis to identify Parkinson's, diabetes, and heart conditions.

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