OPTIMIZING NANOPARTICLE DRUG DELIVERY SYSTEMS FOR CANCER TREATMENT USING MACHINE LEARNING ALGORITHMS

Abstract

Optimizing Nanoparticle Drug Delivery Systems for Cancer Treatment Using Machine Learning Algorithms is the proposed invention. The proposed invention focuses on understanding the functions of Cancer Treatment. The invention focuses on analyzing the parameters of optimization of Nanoparticle's Drug Delivery System using algorithms of Machine Learning Approach.

Specification

Description:[0001] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0002] Machine learning (ML) is a subset of artificial intelligence (AI) that allows machines to learn from data and improve their performance without being explicitly programmed. Machine learning (ML) algorithms use data to make predictions or classifications. They adjust weights to reduce the difference between the model estimate and a known example. Machine learning (ML) is used in many applications, including banking, online shopping, and social media.
[0003] A number of different types of targeted drug delivery for cancer patients that are known in the prior art. For example, the following patents are provided for their supportive teachings and are all incorporated by reference.
[0004] US9026347B2: An expert system manages a power grid wherein charging stations are connected to the power grid, with electric vehicles connected to the charging stations, whereby the expert system selectively backfills power from connected electric vehicles to the power grid through a grid tie inverter (if present) within the charging stations. In more traditional usage, the expert system allows for electric vehicle charging, coupled with user preferences as to charge time, charge cost, and charging station capabilities, without exceeding the power grid capacity at any point. A robust yet accurate state of charge (SOC) calculation method is also presented, whereby initially an open circuit voltage (OCV) based on sampled battery voltages and currents is calculated, and then the SOC is obtained based on a mapping between a previously measured reference OCV (ROCV) and SOC. The OCV-SOC calculation method accommodates likely any battery type with any current profile.
[0005] A nanoparticle is a tiny particle of matter that's less than 100 nanometers in diameter. Nanoparticles are invisible to the naked eye and can have different physical and chemical properties than larger materials. Nanoparticles have a high surface area-to-volume ratio, which makes them capable of producing more efficient reactions. Nanoparticles are used in many industries, including Biomedical, Imaging, Aerospace, Cosmetics and etc. The proposed invention focuses on analyzing the optimization of Nanoparticle's Drug Delivery System through algorithms of Machine Learning Approach.
[0006] Above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, no assertion is made, and as to whether any of the above might be applicable as prior art with regard to the present invention.
[0007] In the view of the foregoing disadvantages inherent in the known types of targeted drug delivery for cancer patients now present in the prior art, the present invention provides an improved system. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved techniques for Optimizing Nanoparticle Drug Delivery Systems for Cancer Treatment Using Machine Learning Algorithms that has all the advantages of the prior art and none of the disadvantages.
SUMMARY OF INVENTION
[0008] In the view of the foregoing disadvantages inherent in the known types of targeted drug delivery for cancer patients now present in the prior art, the present invention provides an improved one. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved techniques for Optimizing Nanoparticle Drug Delivery Systems for Cancer Treatment Using Machine Learning Algorithms which has all the advantages of the prior art and none of the disadvantages.
[0009] The Main objective of the proposed invention is to design & implement a framework of Machine Learning techniques for analyzing the parameters of optimization of Nanoparticle's Drug Delivery System. Optimizing Nanoparticle Drug Delivery Systems for Cancer Treatment is analyzed.
[0010] Yet another important aspect of the proposed invention is to design & implement a framework of Machine Learning techniques that will consider on understanding the functions of Cancer Treatment. Optimizing Nanoparticle Drug Delivery Systems for Cancer Treatment is analyzed by predictive unit. The results of prediction are displayed on the display unit.
[0011] In this respect, before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[0012] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be had to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF DRAWINGS
[0013] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
Figure 1 illustrates the schematic view of Optimizing Nanoparticle Drug Delivery Systems for Cancer Treatment Using Machine Learning Algorithms, according to the embodiment herein.
DETAILED DESCRIPTION OF INVENTION
[0014] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural and logical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
[0015] While the present invention is described herein by way of example using several embodiments and illustrative drawings, those skilled in the art will recognize that the invention is neither intended to be limited to the embodiments of drawing or drawings described, nor intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention covers all modification/s, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the scope of the description or the claims. As used throughout this description, the word "may" be used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Further, the words "a" or "a" mean "at least one" and the word "plurality" means one or more, unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and any additional subject matter not recited, and is not intended to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like are included in the specification solely for the purpose of providing a context for the present invention.
[0016] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same element or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group consisting of", "including", or "is" preceding the recitation of the element or group of elements and vice versa.
[0017] Nanoparticle drug delivery systems are engineered technologies that use nanoparticles for the targeted delivery and controlled release of therapeutic agents. The modern form of a drug delivery system should minimize side-effects and reduce both dosage and dosage frequency. Recently, nanoparticles have aroused attention due to their potential application for effective drug delivery.
[0018] Cancer treatment refers to the medical strategies and therapies used to treat cancer, aiming either to remove, shrink, or stop the growth of cancer cells. The choice of treatment depends on the type, stage, and location of the cancer, as well as the patient's overall health and preferences. Primary types of cancer treatment include Surgery, Radiation Therapy, Chemotherapy, Immunotherapy and etc. The proposed invention focuses on implementing the algorithms of Machine Learning Approach for studying the functions of Cancer Treatment.
[0019] Reference will now be made in detail to the exemplary embodiment of the present disclosure. Before describing the detailed embodiments that are in accordance with the present disclosure, it should be observed that the embodiment resides primarily in combinations arrangement of the system according to an embodiment herein and as exemplified in FIG. 1
[0020] Figure 1 illustrates the schematic view of Optimizing Nanoparticle Drug Delivery Systems for Cancer Treatment Using Machine Learning Algorithms 100. The proposed invention 100 includes a cancer patient 101 which is diagnosed for tumours 102 and store data on analysis unit 103. The machine learning unit 104 will run predictive unit 105 and store results on display unit 106. The results of display unit 106 is fed as input to drug delivery unit 107.
[0021] In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of the arrangement of the system according to an embodiment herein. It will be apparent, however, to one skilled in the art that the present embodiment can be practiced without these specific details. In other instances, structures are shown in block diagram form only in order to avoid obscuring the present invention.
, Claims:1. Optimizing Nanoparticle Drug Delivery Systems for Cancer Treatment Using Machine Learning Algorithms, comprises of:
Machine learning unit;
Predictive unit and
Display unit.
2. Optimizing Nanoparticle Drug Delivery Systems for Cancer Treatment Using Machine Learning Algorithms, according to claim 1, includes a machine learning unit, wherein the machine learning unit will run predictive unit.
3. Optimizing Nanoparticle Drug Delivery Systems for Cancer Treatment Using Machine Learning Algorithms, according to claim 1, includes a predictive unit, wherein the predictive unit will predict the nanoparticle drug delivery system for cancer treatment using machine learning algorithms.
4. Optimizing Nanoparticle Drug Delivery Systems for Cancer Treatment Using Machine Learning Algorithms, according to claim 1, includes a display unit, wherein the display unit will display the results of predictive unit.

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