• Shubhang Joshi
• K.S. Vaidya
• Ajit Deshmukh
• Sujit Deshmukh
• Nikhil Birajdar

Machine Learning has gained considerable attention in recent times across a diapason of exploration disciplines. This growing interest can be attributed to technological advancements and the operation of Machine literacy styles for soothsaying issues grounded on literal data. specially, the field of medical exploration has also embraced Machine literacy in colorful aspects, with a specific emphasis on early discovery of significant conditions similar as madness to enable timely intervention. Just as glucose situations are a individual parameter for diabetes, experimenters have linked specific pointers within the OASIS dataset for diagnosing madness. This study primarily focuses on the task of prognosticating madness, which falls under the order of bracket problems. The primary end of this exploration is to determine the most suitable bracket algorithm for the OASIS dataset. likewise, the study involves fine- tuning the algorithm's hyperactive parameters to optimize its performance in prognosticating the presence of madness in the input data. The findings of the analysis compare different models and eventually recommend the stylish model for madness vaticination. The perpetration results indicate that the Random Forest Classifier emerges as the most effective bracket model for this dataset, achieving a delicacy rate of roughly 73.

Bracket, dataset, supervised learning order, Random Forest algorithm, delicacy

"Early Identification, Monitoring And Treatment Of Dementia In The Aged People Using Machine Learning Predictive Modelling ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.c587-c592, November-2023, Available :http://www.jetir.org/papers/JETIR2311281.pdf

"Early Identification, Monitoring And Treatment Of Dementia In The Aged People Using Machine Learning Predictive Modelling ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppc587-c592, November-2023, Available at : http://www.jetir.org/papers/JETIR2311281.pdf