By adding machine learning (ML) into predictive analytics, the area of personalised healthcare computing has evolved and new approaches to enhance patient outcomes via tailored treatment plans have been generated. This paper examines how healthcare treatments could be tailored and predicted using machine learning methods. It underlines how crucial sophisticated analytics are for enhancing patient care and guiding clinical choices. Treatment is more accurate, more efficient, and better generally when one can predict how a condition will worsen, choose the best course of action for taking drugs, and observe any issues. Like controlled and unstructured learning algorithms, machine learning models have proved to be able to efficiently examine large and complex clinical datasets including electronic health records (EHR) and genetic data. These models identify hidden trends, relationships, and patterns that enable us to forecast individual health paths, identify those at risk, and simplify preventive action. ML also makes it feasible to merge many kinds of data, therefore providing clinicians with a more complete picture of every patient's health and, ultimately, facilitates the provision of more individualised, better treatment. Many facets of healthcare, including management of chronic illnesses, cancer detection, mental health analysis, and new medication discovery, employ predictive models. By helping clinicians make decisions based on data, ML models assist to reduce errors and enhance the flow of treatment. Still, there are issues including concerns about data security, model understanding, and the necessity of consistent frameworks to ensure models are robust and dependable in real-life clinical environments. This work also addresses the moral issues raised by using machine learning algorithms in tailored healthcare. It addresses issues like prejudice, justice, and patient agreement. It emphasises the need of cooperation among legislators, data scientists, and healthcare professionals to maintain developing models so that the whole potential of machine learning in healthcare may be fulfilled.

The integration of photovoltaic system with existing utility plays a vital role to achieve the sustainable development goals set by renewable energy agencies. The grid integration takes place by following IEEE-519 standards. Number of control strategies have already been implemented to maintain the required standard. A number of issues are encountered during the process of synchronization, such as deterioration of quality of power flow due to sudden changes in loads, switching of power semiconductor devices and variation in solar insolation. To address these aforementioned issues a Variable Step Size Sign Data Least Mean Square (VSS-SDLMS) adaptive algorithm is proposed. The proposed controller makes the reference current adaptive to the adversities. In this paper the effective application of signal processing tool i.e.VSS-SDLMS for the power quality issues is well demonstrated by implementing in a three-phase single stage grid connected Photovoltaic (PV) system. The proposed scheme employs to mitigate the harmonic contents produced due to intentional and non-intentional non-linearities in the overall system to a desired level as per IEEE-519 and also it is maintaining the power factor to unity by controlling the reactive power flow. The transient performance of VSS-SDLMS is compared with the conventional LMS based algorithms. The overall system is modelled using MATLAB/SIMULINK.