A Review on Disease Prediction Approach using Data Analytics and Machine Learning Algorithms

Abstract

Presently, the medical industry is facing a serious issue. Machine Learning (ML) is emerging as a solution to analyze large datasets and develop predictive modeling or pattern classification. With the knowledge provided, clinicians could manage unhealthy patients without having a comprehensive of the ailment. As a result, ailments are occasionally misinterpreted and inadequately treated. Researchers teach the system to ascertain the likelihood of the person's ailment based on the symptoms given by the doctor using the existing statistical model. ML is the area of computer science that would be expanding the greatest, and health informatics is quite difficult. A goal of ML is to create predictive algorithms to learn and improve over time. Numerous industries employ the ML approach but the healthcare sector has benefited significantly from them. To increase patient safety and healthcare quality, it provides a wide range of warning and decision-support technologies. The proposed approach, which was created using ML algorithms, aids in earlier disease prediction. Doctors would benefit from they get more acquainted with novel ailments. Due to this knowledge, doctors should be able to treat patients appropriately by switching between illnesses.