• Sinchana C S
• Sushmitha H C
• Shivaleela S

In the field of medical diagnostics, machine learning is gradually becoming more common. This is primarily due to improvements in disease categorization and recognition systems, which can provide information to enable medical specialists in the early discovery of fatal diseases, leading to a noticeably higher patient survival rate. In this study, we use three alternative classification methods, each with its unique set of benefits, to forecast disease in three separate disease databases that are accessible in the UCI repository (heart, lung cancer, COPD, and diabetes).Backward modeling utilizing each dataset's features were chosen using a p-value test. The study's conclusions are in favor of employing machine learning to identify diseases early. The study ‘Preventive Healthcare and Corporate Female Workforce’ also said that long hours and working under strict deadlines cause up to 75% of working women to suffer from depression or general anxiety disorder, compared to women with lesser levels of psychological demand at work. Machine learning can be used in the medical industry to diagnose, detect, and forecast a variety of diseases. This paper's major objective is to give clinicians a tool for early heart disease detection. In turn, this will assist in giving patients appropriate care and preventing negative outcomes.

INTRODUCTION, LITERATURE SURVEY, EXISTING SYSTEM, ACTIVITY DIAGRAM, ER DIAGRAM, RESULT, CONCLUSION, FUTURE ENHANCEMENTS, REFERNCES

"DISIGNING DISEASE PREDICTION MODEL USING MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.b334-b338, July-2022, Available :http://www.jetir.org/papers/JETIR2207136.pdf

"DISIGNING DISEASE PREDICTION MODEL USING MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppb334-b338, July-2022, Available at : http://www.jetir.org/papers/JETIR2207136.pdf